28 results
Search Results
2. Advances in artificial neural networks, machine learning and computational intelligence Selected papers from the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
- Published
- 2019
3. A survey on kinship verification
- Author
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Wang, W., You, S., Karaoglu, S., Gevers, T., Computer Vision (IVI, FNWI), and Amsterdam Machine Learning lab (IVI, FNWI)
- Subjects
Artificial Intelligence ,Cognitive Neuroscience ,Computer Science Applications - Abstract
In this survey, kinship verification is defined as the automatic process of verifying whether two or more persons are blood relatives (kin) by analyzing images of their faces. Kinship verification is an important research field in computer vision with many applications such as finding missing persons, family album organization, and online image search. Although substantial progress has been made in kinship verification in the past decade, there are still challenges such as intrinsic (face i.e., differences in facial appearance) and extrinsic (acquisition i.e., varying imaging conditions) problems. And there is still a demand for more diverse datasets.Therefore, this paper provides a survey on kinship verification methods and datasets. The survey starts with the definition of kinship verification and its corresponding intrinsic and extrinsic challenges. Then, an overview of kinship verification methods and datasets is given. Finally, a new multi-modal dataset (Nemo-Kinship Dataset) is proposed as a benchmark dataset addressing large inter-subject age variations consisting of 4216 videos of 248 persons from 85 families. The newly collected dataset is used to systematically test and analyze state-of-the-art methods.
- Published
- 2023
4. Multi-view Contour-constrained Transformer Network for Thin-cap Fibroatheroma Identification
- Author
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Sijie Liu, Jingmin Xin, Jiayi Wu, Yangyang Deng, Ruisheng Su, Wiro J. Niessen, Nanning Zheng, Theo van Walsum, and Radiology & Nuclear Medicine
- Subjects
IVOCT ,Transformer ,Artificial Intelligence ,Cognitive Neuroscience ,Multi-view learning ,TCFA ,Plaque identification ,Computer Science Applications - Abstract
Identification and detection of thin-cap fibroatheroma (TCFA) from intravascular optical coherence tomography (IVOCT) images is critical for treatment of coronary heart diseases. Recently, deep learning methods have shown promising successes in TCFA identification. However, most methods usually do not effectively utilize multi-view information or incorporate prior domain knowledge. In this paper, we propose a multi-view contour-constrained transformer network (MVCTN) for TCFA identification in IVOCT images. Inspired by the diagnosis process of cardiologists, we use contour constrained self-attention modules (CCSM) to emphasize features corresponding to salient regions (i.e., vessel walls) in an unsupervised manner and enhance the visual interpretability based on class activation mapping (CAM). Moreover, we exploit transformer modules (TM) to build global-range relations between two views (i.e., polar and Cartesian views) to effectively fuse features at multiple feature scales. Experimental results on a semi-public dataset and an in-house dataset demonstrate that the proposed MVCTN outperforms other single-view and multi-view methods. Lastly, the proposed MVCTN can also provide meaningful visualization for cardiologists via CAM.
- Published
- 2023
5. Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis
- Author
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Amir Hussain, Newton Howard, Soujanya Poria, Haiyun Peng, and Erik Cambria
- Subjects
Computer science ,Cognitive Neuroscience ,SVM ,Convolutional neural network ,02 engineering and technology ,Affect (psychology) ,Machine learning ,computer.software_genre ,Social web ,Artificial Intelligence ,Margin (machine learning) ,0202 electrical engineering, electronic engineering, information engineering ,Multimodal sentiment analysis ,Emotion ,MKL ,Multiple kernel learning ,business.industry ,Deep learning ,Sentiment analysis ,020206 networking & telecommunications ,Classification ,Computer Science Applications ,Support vector machine ,Sentiment ,020201 artificial intelligence & image processing ,ELM ,Artificial intelligence ,business ,computer - Abstract
The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers, smartphones, tablets, university projectors and security cameras, the amount of multimodal content on the Web has been growing exponentially, and with that comes the need for decoding such information into useful knowledge. In this paper, a multimodal affective data analysis framework is proposed to extract user opinion and emotions from video content. In particular, multiple kernel learning is used to combine visual, audio and textual modalities. The proposed framework outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10–13% and 3–5% accuracy on polarity detection and emotion recognition, respectively. The paper also proposes an extensive study on decision-level fusion.
- Published
- 2017
6. Special issue on multi-objective reinforcement learning
- Author
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Peter Vamplew, Marco A. Wiering, Madalina M. Drugan, Madhu Chetty, Security, and Artificial Intelligence
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0209 industrial biotechnology ,Computer science ,Management science ,Cognitive Neuroscience ,02 engineering and technology ,Energy consumption ,Maximization ,Computer Science Applications ,multi-objective ,020901 industrial engineering & automation ,Artificial Intelligence ,Reinforcement learning ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Algorithm design ,SDG 7 - Affordable and Clean Energy ,SDG 7 – Betaalbare en schone energie - Abstract
Many real-life problems involve dealing with multiple objectives. For example, in network routing the criteria may consist of energy consumption, latency, and channel capacity, which are in essence conflicting objectives. As in many problems there may be multiple (conflicting) objectives, there usually does not exist a single optimal solution. In those cases, it is desirable to obtain a set of trade-off solutions between the objectives. This problem has in the last decade also gained the attention of many researchers in the field of reinforcement learning (RL). RL addresses sequential decision problems in initially (possibly) unknown stochastic environments. The goal is the maximization of the agent's reward in an environment that is not always completely observable. The purpose of this special issue is to obtain a broader picture on the algorithmic techniques at the confluence between multi-objective optimization and reinforcement learning. The growing interest in multi-objective reinforcement learning (MORL) was reflected in the quantity and quality of submissions received for this special issue. After a rigorous review process, seven papers were accepted for publication, and they reflect the diversity of research being carried out within this emerging field of research. The accepted papers consider many different aspects of algorithmic design and the evaluation and this editorial puts them in a unified framework.
- Published
- 2017
7. Fully distributed quantized secure bipartite consensus control of nonlinear multiagent systems subject to denial-of-service attacks
- Author
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Qiang Wang, Lorenzo Zino, Dayu Tan, Jiapeng Xu, Weimin Zhong, and Discrete Technology and Production Automation
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Fully distributed control ,Nonlinear multi-agent systems (MASs) ,Secure bipartite (bounded) consensus ,Observer-based control ,Quantized communication ,DoS attacks ,Artificial Intelligence ,Cognitive Neuroscience ,Computer Science Applications - Abstract
This paper is intended to solve the fully distributed secure bipartite consensus problem of nonlinear multi-agent systems (MASs) with quantized information under Denial-of-Service (DoS) attacks. The attacks, which constrained on attack frequency and duration are studied. Firstly, we propose a novel secure output feedback control protocol integrated of the logarithmic quantizer and relative output measurements of neighboring agents, which can realize secure control under DoS attacks by choosing the design parameters correctly. Secondly, an adaptive control protocol that includes dynamic coupling strengths into the control law and the state observer function is developed. Contrast to the single adaptive control strategy, two adaptive couplings constructed in sensor-to-observer, and controller-to-actuator channels, respectively, which can alleviate the burden of the limited bandwidth and energy consumption more effectively. Furthermore, this control strategy with dynamic coupling gains is fully distributed, under which agents are not required to know a priori knowledge of any global information and the quantizer only needs to quantize the output state error information of agents. Then, theoretical guarantees on the effectiveness of the proposed controllers in steering the system to a secure bipartite (bounded) consensus under quantized output measurements and intermittent DoS attacks are derived. Finally, the numerical simulation inspired by a real-world physical network system is developed to verify the usefulness of the presented controllers.
- Published
- 2022
8. Bipartite Consensus for a Class of Nonlinear Multi-agent Systems Under Switching Topologies
- Author
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Qiang Wang, Wangli He, Lorenzo Zino, Dayu Tan, Weimin Zhong, and Discrete Technology and Production Automation
- Subjects
Computer Science::Multiagent Systems ,Output feedback control ,Bipartite consensus ,Nonlinear MASs ,Artificial Intelligence ,Cognitive Neuroscience ,Directed fixed and switching topologies ,Disturbance observer ,Computer Science Applications - Abstract
This paper considers the leader-following bipartite consensus for a class of nonlinear multi-agent systems (MASs) subject to exogenous disturbances under directed fixed and switching topologies, respectively. Firstly, two new output feedback control protocols involving signs of link weights are introduced based on relative output measurements of neighboring agents. In order to estimate the disturbances produced by an exogenous system, a disturbance observer-based approach is developed. Then, sufficient conditions for leader-following bipartite consensus with directed fixed topologies are derived. Furthermore, by assuming that each switching topology contains a directed spanning tree, it is proved that the leader-following bipartite consensus can be realized with the designed output feedback control protocol if the dwell time is larger than a non-negative threshold. Finally, numerical simulations inspired by a real-world DC motors are provided to illustrate the effectiveness of the proposed controllers.
- Published
- 2022
9. OPELM and OPKNN in long-term prediction of time series using projected input data
- Author
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Qi Yu, Dušan Sovilj, Yoan Miche, Eric Séverin, Antti Sorjamaa, UMR CNRS 8179, and Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS)
- Subjects
ta113 ,Computer science ,Cognitive Neuroscience ,long-term time series prediction ,projection ,HannanQuinn information criterion ,[SHS.ECO]Humanities and Social Sciences/Economics and Finance ,computer.software_genre ,OPKNN ,Computer Science Applications ,Data set ,OPELM ,Artificial Intelligence ,Genetic algorithm ,genetic algorithm ,Data mining ,Time series ,Projection (set theory) ,Long-term prediction ,computer ,Extreme learning machine ,Curse of dimensionality ,Hannan–Quinn information criterion ,delta test - Abstract
Long-term time series prediction is a difficult task. This is due to accumulation of errors and inherent uncertainties of a long-term prediction, which leads to deteriorated estimates of the future instances. In order to make accurate predictions, this paper presents a methodology that uses input processing before building the model. Input processing is a necessary step due to the curse of dimensionality, where the aim is to reduce the number of input variables or features. In the paper, we consider the combination of the delta test and the genetic algorithm to obtain two aspects of reduction: scaling and projection. After input processing, two fast models are used to make the predictions: optimally pruned extreme learning machine and optimally pruned k-nearest neighbors. Both models have fast training times, which makes them suitable choice for direct strategy for long-term prediction. The methodology is tested on three different data sets: two time series competition data sets and one financial data set.
- Published
- 2010
10. Mask-MCNet
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Farhad Ghazvinian Zanjani, Arash Pourtaherian, Svitlana Zinger, David Anssari Moin, Frank Claessen, Teo Cherici, Sarah Parinussa, Peter H.N. de With, Center for Care & Cure Technology Eindhoven, Video Coding & Architectures, Signal Processing Systems, Eindhoven MedTech Innovation Center, and EAISI Health
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0209 industrial biotechnology ,Intersection (set theory) ,Computer science ,business.industry ,Cognitive Neuroscience ,Deep learning ,Instance object segmentation ,Point cloud ,Context (language use) ,02 engineering and technology ,Computer Science Applications ,Image (mathematics) ,3D point cloud ,020901 industrial engineering & automation ,Artificial Intelligence ,Minimum bounding box ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Segmentation ,Artificial intelligence ,Intra-oral scan ,business ,Image resolution - Abstract
Computational dentistry uses computerized methods and mathematical models for dental image analysis. One of the fundamental problems in computational dentistry is accurate tooth instance segmentation in high-resolution mesh data of intra-oral scans (IOS). This paper presents a new computational model based on deep neural networks, called Mask-MCNet, for end-to-end learning of tooth instance segmentation in 3D point cloud data of IOS. The proposed Mask-MCNet localizes each tooth instance by predicting its 3D bounding box and simultaneously segments the points that belong to each individual tooth instance. The proposed model processes the input raw 3D point cloud in its original spatial resolution without employing a voxelization or down-sampling technique. Such a characteristic preserves the finely detailed context in data like fine curvatures in the border between adjacent teeth and leads to a highly accurate segmentation as required for clinical practice (e.g. orthodontic planning). The experiments show that the Mask-MCNet outperforms state-of-the-art models by achieving 98% Intersection over Union (IoU) score on tooth instance segmentation which is very close to human expert performance.
- Published
- 2021
11. Conformal Feature-Selection Wrappers and ensembles for negative-transfer avoidance
- Author
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Shuang Zhou, Ralf Peeters, Xi Wu, Gijs Schoenmakers, Evgueni Smirnov, DKE Scientific staff, RS: FSE DACS, RS: FSE DACS Mathematics Centre Maastricht, and RS: FSE MaCSBio
- Subjects
0209 industrial biotechnology ,Computer science ,Cognitive Neuroscience ,Negative transfer ,Conformal map ,Feature selection ,02 engineering and technology ,Wrappers ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Feature Selection ,Ensembles ,ELDERLY-PATIENTS ,Selection (genetic algorithm) ,CONGESTIVE-HEART-FAILURE ,business.industry ,Instance transfer ,Pattern recognition ,Computer Science Applications ,Character (mathematics) ,020201 artificial intelligence & image processing ,TRIAL ,Artificial intelligence ,Conformal prediction ,business ,STANDARD MEDICAL THERAPY - Abstract
In this paper we propose two methods for instance transfer based on conformal prediction. As a distinctive character, both of the methods are model independent and combine feature selection and source-instance selection to avoid negative transfer. The methods have been tested experimentally for different types of classification model on several benchmark data sets. The experimental results demonstrate that the new methods are capable of outperforming significantly standard instance transfer methods. (C) 2019 Elsevier B.V. All rights reserved.
- Published
- 2020
12. A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction
- Author
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Huayi Wu, Zhaoyu Zhang, Zhipeng Gui, Dehua Peng, Kunxiaojia Yuan, Fa Li, Yichen Lei, Siyu Tian, Jianya Gong, and Yunzeng Sun
- Subjects
0209 industrial biotechnology ,Human mobility ,Computer science ,Cognitive Neuroscience ,Individual mobility ,LSTM encoder-decoder model ,02 engineering and technology ,computer.software_genre ,Article ,Computer Science Applications ,020901 industrial engineering & automation ,Temporal attention ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Sequence prediction ,020201 artificial intelligence & image processing ,Travel regularity ,Data mining ,Encoder decoder ,Baseline (configuration management) ,Mobility prediction ,computer - Abstract
Highlights • A hierarchical temporal attention based model is proposed to support short-term and long-term human mobility sequence prediction. • The proposed hierarchical temporal attention incorporates individual mobility patterns into the model architecture. • The model is compared with four baseline methods on individual trajectory datasets with varying degree of traveling uncertainty. • Experiments demonstrate the outperformance of the proposed method using three evaluation metrics. • The proposed model uncovers individual frequential and periodical mobility patterns in an interpretable manner., Prediction of individual mobility is crucial in human mobility related applications. Whereas, existing research on individual mobility prediction mainly focuses on next location prediction and short-term dependencies between traveling locations. Long-term location sequence prediction is of great importance for long-time traffic planning and location advertising, and long-term dependencies exist as individual mobility regularity typically occurs daily and weekly. This paper proposes a novel hierarchical temporal attention-based LSTM encoder-decoder model for individual location sequence prediction. The proposed hierarchical attention mechanism captures both long-term and short-term dependencies underlying in individual longitudinal trajectories, and uncovers frequential and periodical mobility patterns in an interpretable manner by incorporating the calendar cycle of individual travel regularities into location prediction. More specifically, the hierarchical attention consists of local temporal attention to identify highly related locations in each day, and global temporal attention to discern important travel regularities over a week. Experiments on individual trajectory datasets with varying degree of traveling uncertainty demonstrate that our method outperforms four baseline methods on three evaluation metrics. In addition, we explore the interpretability of the proposed model in understanding individual daily, and weekly mobility patterns by visualizing the temporal attention weights and frequent traveling patterns associated with locations.
- Published
- 2020
13. Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface
- Author
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Dheeraj Rathee, Shang-Ming Zhou, Haider Raza, Hubert Cecotti, and Girijesh Prasad
- Subjects
FOS: Computer and information sciences ,0209 industrial biotechnology ,Covariate shift ,CSE, Covariate shift estimation ,Computer science ,CSV, Covariate shift validation ,EEG, Electroencephalography ,SSL, Semi-supervised learning ,NSL, Non-stationary learning ,02 engineering and technology ,Electroencephalography ,DWEC, Dynamically weighted ensemble classification ,Machine Learning (cs.LG) ,020901 industrial engineering & automation ,MI, Motor imagery ,CSP, Common spatial pattern ,Statistics - Machine Learning ,RSM, Random subspace method ,0202 electrical engineering, electronic engineering, information engineering ,CS, Covariate shift ,CSW, Covariate shift warning ,BCI, Brain-computer-interface ,medicine.diagnostic_test ,FB, Frequency band ,PWKNN, Probabilistic weighted K-nearest neighbour ,FBCSP, Filter bank common spatial pattern ,Computer Science Applications ,CSE-UAEL, CSE-based unsupervised adaptive ensemble learning ,Principal component analysis ,020201 artificial intelligence & image processing ,QA75 ,KNN, K-nearest-neighbors ,Cognitive Neuroscience ,Machine Learning (stat.ML) ,ERS, Desynchronization ,Article ,Motor imagery ,Artificial Intelligence ,Ensemble learning ,Covariate ,ERD, Synchronization ,medicine ,Brain–computer interface ,PCA, Principal component analysis ,Brain-computer interface (BCI) ,business.industry ,Pattern recognition ,Electroencephalogram (EEG) ,CSA, Covariate shift adaptation ,Computer Science - Learning ,ComputingMethodologies_PATTERNRECOGNITION ,RC0321 ,Non-stationary learning ,Artificial intelligence ,business ,Classifier (UML) ,EWMA, exponential weighted moving average ,LDA, Linear discriminant analysis - Abstract
The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications., 28 Pages, 3 figures, Neurocomputing
- Published
- 2019
14. Stochastic Discriminant Analysis for Linear Supervised Dimension Reduction
- Author
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Juha Karhunen, Mika Juuti, Francesco Corona, Adj. Prof Asokan N. group, Department of Computer Science, Aalto-yliopisto, and Aalto University
- Subjects
Kullback–Leibler divergence ,Linear projection ,Cognitive Neuroscience ,Linear model ,02 engineering and technology ,01 natural sciences ,010104 statistics & probability ,Information visualization ,Artificial Intelligence ,Joint probability distribution ,0202 electrical engineering, electronic engineering, information engineering ,0101 mathematics ,Divergence (statistics) ,Mathematics ,ta113 ,business.industry ,Dimensionality reduction ,Distance based probabilities ,Supervised learning ,Pattern recognition ,Linear discriminant analysis ,Classification ,Computer Science Applications ,Dimension reduction ,Probability distribution ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
In this paper, we consider a linear supervised dimension reduction method for classification settings: stochastic discriminant analysis (SDA). This method matches similarities between points in the projection space with those in a response space. The similarities are represented by transforming distances between points to joint probabilities using a transformation which resembles Student’s t-distribution. The matching is done by minimizing the Kullback–Leibler divergence between the two probability distributions. We compare the performance of our SDA method against several state-of-the-art methods for supervised linear dimension reduction. In our experiments, we found that the performance of the SDA method is often better and typically at least equal to the compared methods. We have made experiments with various types of data sets having low, medium, or high dimensions and quite different numbers of samples, and with both sparse and dense data sets. If there are several classes in the studied data set, the low-dimensional projections computed using our SDA method provide often higher classification accuracies than the compared methods.
- Published
- 2018
15. Learning to Rank Images for Complex Queries in Concept-based Search
- Author
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Shuaiqiang Wang, Jun Ma, Jialie Shen, Zhumin Chen, and Chaoran Cui
- Subjects
Theoretical computer science ,Cognitive Neuroscience ,02 engineering and technology ,factorization machine ,Ranking (information retrieval) ,Set (abstract data type) ,Artificial Intelligence ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Relevance (information retrieval) ,tiedonhaku ,kuvat ,Mathematics ,learning to rank ,ta113 ,Internet ,Concept search ,Rank (computer programming) ,kuvahaku ,Computer Science Applications ,complex query ,020201 artificial intelligence & image processing ,Learning to rank ,Pairwise comparison ,concept-based image search ,Semantic gap - Abstract
Concept-based image search is an emerging search paradigm that utilizes a set of concepts as intermediate semantic descriptors of images to bridge the semantic gap. Typically, a user query is rather complex and cannot be well described using a single concept. However, it is less effective to tackle such complex queries by simply aggregating the individual search results for the constituent concepts. In this paper, we propose to introduce the learning to rank techniques to concept-based image search for complex queries. With freely available social tagged images, we first build concept detectors by jointly leveraging the heterogeneous visual features. Then, to formulate the image relevance, we explicitly model the individual weight of each constituent concept in a complex query. The dependence among constituent concepts, as well as the relatedness between query and non-query concepts, are also considered through modeling the pairwise concept correlations in a factorization way. Finally, we train our model to directly optimize the image ranking performance for complex queries under a pairwise learning to rank framework. Extensive experiments on two benchmark datasets well verified the promise of our approach. peerReviewed
- Published
- 2018
16. Category kappas for agreement between fuzzy classifications
- Author
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Matthijs J. Warrens and Research and Evaluation of Educational Effectiveness
- Subjects
Fuzzy classification ,Category statistics ,Cognitive Neuroscience ,media_common.quotation_subject ,Interobserver agreement ,Brain tissue ,01 natural sciences ,Fuzzy logic ,010104 statistics & probability ,Cohen's kappa ,0504 sociology ,Artificial Intelligence ,Mathematics::Category Theory ,Statistics ,0101 mathematics ,Statistic ,media_common ,Mathematics ,business.industry ,05 social sciences ,050401 social sciences methods ,Pattern recognition ,Agreement ,Computer Science Applications ,Mathematics::Logic ,Artificial intelligence ,business ,Kappa ,MRI - Abstract
The kappa statistic is a widely used as a measure for quantifying agreement between two nominal classifications. The statistic has been extended to the case of two normalized fuzzy classifications. In this paper we define category kappas for quantifying agreement on a particular category of two normalized fuzzy classifications. The overall fuzzy kappa is a weighted average of the proposed category kappas. Since the value of the overall kappa lies between the minimum and maximum values of the category kappas, the overall kappa, in a way, summarizes the agreement reflected in the category kappas. The overall kappa meaningfully reflects the degree of agreement between the fuzzy classifications if the category kappas are approximately equal. If this is not the case, it is more informative to report the category kappas. HighlightsFuzzy kappas for quantifying agreement on a particular category are defined.The fuzzy kappa of Dou et al. is a weighted average of the proposed category kappas.With two categories the category kappas and overall kappa coincide.Adding all other membership functions presents an alternative definition.
- Published
- 2016
17. A novel one-layer recurrent neural network for the l1-regularized least square problem
- Author
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S. Hamid Mousavi, Majid Mohammadi, Wout Hofman, and Yao-Hua Tan
- Subjects
0301 basic medicine ,Lyapunov function ,Mathematical optimization ,Total variation ,Artificial neural network ,Computer science ,Cognitive Neuroscience ,Lyapunov ,Recurrent neural network ,Total variation denoising ,Convex ,Regularization (mathematics) ,Least squares ,Computer Science Applications ,03 medical and health sciences ,symbols.namesake ,030104 developmental biology ,Quadratic equation ,l-regularization ,Artificial Intelligence ,symbols ,Differentiable function ,Variable (mathematics) - Abstract
The l1-regularized least square problem has been considered in diverse fields. However, finding its solution is exacting as its objective function is not differentiable. In this paper, we propose a new one-layer neural network to find the optimal solution of the l1-regularized least squares problem. To solve the problem, we first convert it into a smooth quadratic minimization by splitting the desired variable into its positive and negative parts. Accordingly, a novel neural network is proposed to solve the resulting problem, which is guaranteed to converge to the solution of the problem. Furthermore, the rate of the convergence is dependent on a scaling parameter, not to the size of datasets. The proposed neural network is further adjusted to encompass the total variation regularization. Extensive experiments on the l1 and total variation regularized problems illustrate the reasonable performance of the proposed neural network.
- Published
- 2018
18. An effective framework for supervised dimension reduction
- Author
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Duy Khuong Nguyen, Khoat Than, and Tu Bao Ho
- Subjects
Topic model ,Exploit ,business.industry ,Cognitive Neuroscience ,Dimensionality reduction ,Nonlinear dimensionality reduction ,supervised dimension reduction ,Machine learning ,computer.software_genre ,Computer Science Applications ,Data point ,Artificial Intelligence ,Margin (machine learning) ,Encoding (memory) ,Scalability ,sparse modeling ,Data mining ,Artificial intelligence ,business ,topic model ,computer ,Mathematics - Abstract
We consider supervised dimension reduction (SDR) for problems with discrete inputs. Existing methods are computationally expensive, and often do not take the local structure of data into consideration when searching for a low-dimensional space. In this paper, we propose a novel framework for SDR with the aims that it can inherit scalability of existing unsupervised methods, and that it can exploit well label information and local structure of data when searching for a new space. The way we encode local information in this framework ensures three effects: preserving inner-class local structure, widening inter-class margin, and reducing possible overlap between classes. These effects are vital for success in practice. Such an encoding helps our framework succeed even in cases that data points reside in a nonlinear manifold, for which existing methods fail. The framework is general and flexible so that it can be easily adapted to various unsupervised topic models. We then adapt our framework to three unsupervised models which results in three methods for SDR. Extensive experiments on 10 practical domains demonstrate that our framework can yield scalable and qualitative methods for SDR. In particular, one of the adapted methods can perform consistently better than the state- of-the-art method for SDR while enjoying 30-450 times faster speed.
- Published
- 2014
19. Elimination of Filtering Indeterminacyin Blind Source Separation
- Author
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Matsuoka, K
- Subjects
minimal distortion principle ,blind source separation ,convolutive mixture ,linear constraint - Abstract
In blind source separation, the definition of sources has an inherent indeterminacy; any linear transform of a source signal can be considered another form of it. Due to this indeterminacy, the designing of a separator has a certain freedom. This paper proposes an idea for eliminating the indeterminacy, i.e., a principle for uniquely normalizing the separator. The output of the separator based on the principle undergoes the least distortion in the demixing process. The method has some good properties: no signal distortion, no fluctuation for nonstationary sources, and simple implementation.
- Published
- 2008
20. Agglomerative independent variable group analysis
- Author
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Antti Honkela, Jeremias Seppä, and Esa Alhoniemi
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Independent variable group analysis ,Variables ,Hierarchy (mathematics) ,business.industry ,Variational Bayesian learning ,Cognitive Neuroscience ,media_common.quotation_subject ,Feature selection ,Pattern recognition ,Mutual information ,Bayesian inference ,Measure (mathematics) ,Hierarchical clustering ,Computer Science Applications ,Group analysis ,Artificial Intelligence ,Variable grouping ,Artificial intelligence ,business ,Mathematics ,media_common - Abstract
Independent variable group analysis (IVGA) is a method for grouping dependent variables together while keeping mutually independent or weakly dependent variables in separate groups. In this paper two variants of an agglomerative method for learning a hierarchy of IVGA groupings are presented. The method resembles hierarchical clustering, but the choice of clusters to merge is based on variational Bayesian model comparison. This is approximately equivalent to using a distance measure based on a model-based approximation of mutual information between groups of variables. The approach also allows determining optimal cutoff points for the hierarchy. The method is demonstrated to find sensible groupings of variables that can be used for feature selection and ease construction of a predictive model.
- Published
- 2008
21. Classification of neural signals from sparse autoregressive features
- Author
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Diego Vidaurre, Pedro Larrañaga, and Concha Bielza
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Informática ,Computer science ,business.industry ,Cognitive Neuroscience ,Feature selection ,Pattern recognition ,Linear discriminant analysis ,Logistic regression ,Computer Science Applications ,Support vector machine ,ComputingMethodologies_PATTERNRECOGNITION ,Binary classification ,Autoregressive model ,Artificial Intelligence ,Statistics::Methodology ,Artificial intelligence ,business - Abstract
This paper introduces a signal classification framework that can be used for brain-computer interface design. The actual classification is performed on sparse autoregressive features. It can use any well-known classification algorithm, such as discriminant analysis, linear logistic regression and support vector machines. The autoregressive coefficients of all signals and channels are simultaneously estimated by the group lasso, and the estimation is guided by the classification performance. Thanks to the variable selection capability of the group lasso, the framework can drop individual autoregressive coefficients that are useless in the prediction stage. Also, the framework is relatively insensitive to the chosen autoregressive order. We devise an efficient algorithm to solve this problem. We test our approach on Keirn and Aunon's data, used for binary classification of electroencephalogram signals, achieving promising results. © 2013.
- Published
- 2013
22. Modelling visual search experiments: the selective attention for identification model (SAIM)
- Author
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Giovanni Di Virgilio, Glyn W. Humphreys, and Dietmar Heinke
- Subjects
Visual search ,Computer science ,business.industry ,Cognitive Neuroscience ,Neuropsychology ,Object (computer science) ,Visual search tasks ,Machine learning ,computer.software_genre ,Computer Science Applications ,Identification (information) ,Artificial Intelligence ,Similarity (psychology) ,Artificial intelligence ,Selective attention ,business ,computer - Abstract
We have presented a computational model called selective attention for identification model (SAIM), that can account for a broad range of psychological and neuropsychological phenomena on attention [1]. In this paper we report on work that extends SAIM to model data from visual search tasks. The results show that SAIM can capture important aspects of findings in visual search experiments, including variations of search slopes with the similarity between targets and distractors. SAIM is also capable of simulating experimental findings for redundant targets, including the violation of the Miller inequality. SAIM explains these experimental findings as the consequence of competitive processes involved in object identification. © 2002 Published by Elsevier Science B.V.
- Published
- 2002
23. Deep prediction networks
- Author
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Gianluigi Pillonetto and Alberto Dalla Libera
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0209 industrial biotechnology ,Nonlinear System identification ,Long-term predictions ,System stability ,Nonparametric estimation ,Deep networks ,Computer science ,Cognitive Neuroscience ,Complex system ,Stability (learning theory) ,02 engineering and technology ,Machine learning ,computer.software_genre ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Flexibility (engineering) ,Nonlinear System identificationLong-term predictionsSystem stabilityNonparametric estimationDeep networks ,business.industry ,System identification ,Computer Science Applications ,Nonlinear system ,Identification (information) ,Kernel (image processing) ,Benchmark (computing) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer - Abstract
The challenge for next generation system identification is to build new flexible models and estimators able to simulate complex systems. This task is especially difficult in the nonlinear setting. In fact, in many real applications the performance of long-term predictors may be severely affected by stability problems arising due to the output feedback. For this purpose, also the use of deep networks, which are having much success to solve classification problems, has not led so far to any significant cross-fertilization with system identification. This paper proposes a novel procedure based on a hierarchical architecture, which we calldeep prediction network, whose flexibility is used to favor the identification of stable systems. In particular, its structure contains layers whose aim is to improve long-term predictions, with complexity controlled by a kernel-based strategy. The usefulness of the new approach is demonstrated through many examples, including important real benchmark problems taken from the system identification literature
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24. WordNet2Vec: Corpora agnostic word vectorization method
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Maciej Piasecki, Roman Bartusiak, Tomasz Kajdanowicz, Przemysław Kazienko, and Łukasz Augustyniak
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FOS: Computer and information sciences ,natural language structuring, WordNet, WordNet2Vec, vectorization, network transformation, sentiment analysis, transfer learning, big data, complex networks ,Computer science ,Computer Science - Artificial Intelligence ,Cognitive Neuroscience ,WordNet ,Context (language use) ,02 engineering and technology ,computer.software_genre ,Set (abstract data type) ,Artificial Intelligence ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Cluster analysis ,Abstraction (linguistics) ,Computer Science - Computation and Language ,business.industry ,Sentiment analysis ,Computer Science Applications ,Artificial Intelligence (cs.AI) ,Computer Science - Distributed, Parallel, and Cluster Computing ,020201 artificial intelligence & image processing ,Distributed, Parallel, and Cluster Computing (cs.DC) ,Artificial intelligence ,business ,Computation and Language (cs.CL) ,computer ,Natural language ,Word (computer architecture) ,Natural language processing - Abstract
A complex nature of big data resources demands new methods for structuring especially for textual content. WordNet is a good knowledge source for comprehensive abstraction of natural language as its good implementations exist for many languages. Since WordNet embeds natural language in the form of a complex network, a transformation mechanism WordNet2Vec is proposed in the paper. It creates vectors for each word from WordNet. These vectors encapsulate general position - role of a given word towards all other words in the natural language. Any list or set of such vectors contains knowledge about the context of its component within the whole language. Such word representation can be easily applied to many analytic tasks like classification or clustering. The usefulness of the WordNet2Vec method was demonstrated in sentiment analysis, i.e. classification with transfer learning for the real Amazon opinion textual dataset., 29 pages, 16 figures, submitted to journal
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25. Fusion paradigms in cognitive technical systems for human–computer interaction
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Felix Schüssel, Klaus Dietmayer, Michael Weber, Florian Nothdurft, Thilo Hörnle, Thomas Geier, Friedhelm Schwenker, Gerald Krell, Stephan Reuter, Susanne Biundo, Frank Honold, Michael Glodek, Wolfgang Minker, and Günther Palm
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Fusion ,Human–computer interaction ,business.industry ,Computer science ,Cognitive Neuroscience ,Technical systems ,Scene understanding ,Cognition ,Machine learning ,computer.software_genre ,Computer Science Applications ,Artificial Intelligence ,Robustness (computer science) ,Cognitive technical systems ,Artificial intelligence ,business ,computer - Abstract
Recent trends in human–computer interaction (HCI) show a development towards cognitive technical systems (CTS) to provide natural and efficient operating principles. To do so, a CTS has to rely on data from multiple sensors which must be processed and combined by fusion algorithms. Furthermore, additional sources of knowledge have to be integrated, to put the observations made into the correct context. Research in this field often focuses on optimizing the performance of the individual algorithms, rather than reflecting the requirements of CTS. This paper presents the information fusion principles in CTS architectures we developed for Companion Technologies. Combination of information generally goes along with the level of abstractness, time granularity and robustness, such that large CTS architectures must perform fusion gradually on different levels — starting from sensor-based recognitions to highly abstract logical inferences. In our CTS application we sectioned information fusion approaches into three categories: perception-level fusion, knowledge-based fusion and application-level fusion. For each category, we introduce examples of characteristic algorithms. In addition, we provide a detailed protocol on the implementation performed in order to study the interplay of the developed algorithms.
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26. Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot
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Changjing Shang, Longzhi Yang, Fei Chao, Chih-Min Lin, Changle Zhou, and Ruiqi Wu
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0209 industrial biotechnology ,Computer science ,Character (computing) ,G400 ,Cognitive Neuroscience ,media_common.quotation_subject ,02 engineering and technology ,G600 ,Computer Science Applications ,Robot control ,020901 industrial engineering & automation ,Calligraphy ,Artificial Intelligence ,Human–computer interaction ,0202 electrical engineering, electronic engineering, information engineering ,Key (cryptography) ,Reinforcement learning ,Robot ,020201 artificial intelligence & image processing ,Motion planning ,Function (engineering) ,media_common - Abstract
As a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human–robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a large amount of implementation work from human engineers. In addition, the written results cannot be accurately evaluated. To overcome these limitations, in this paper, we propose a robotic calligraphy model that combines a generative adversarial network (GAN) and deep reinforcement learning to enable a calligraphy robot to learn to write Chinese character strokes directly from images captured from Chinese calligraphic textbooks. In our proposed model, to automatically establish an aesthetic evaluation system for Chinese calligraphy, a GAN is first trained to understand and reconstruct stroke images. Then, the discriminator network is independently extracted from the trained GAN and embedded into a variant of the reinforcement learning method, the “actor-critic model”, as a reward function. Thus, a calligraphy robot adopts the improved actor-critic model to learn to write multiple character strokes. The experimental results demonstrate that the proposed model successfully allows a calligraphy robot to write Chinese character strokes based on input stroke images. The performance of our model, compared with the state-of-the-art deep reinforcement learning method, shows the efficacy of the combination approach. In addition, the key technology in this work shows promise as a solution for robotic autonomous assembly.
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27. DK-CNNs: Dynamic kernel convolutional neural networks
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Chih-Min Lin, Changjing Shang, Jialin Liu, Changle Zhou, and Fei Chao
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0209 industrial biotechnology ,Computer science ,Cognitive Neuroscience ,Computer Science::Neural and Evolutionary Computation ,02 engineering and technology ,Latent variable ,Convolutional neural network ,Computer Science Applications ,Convolution ,Kernel (linear algebra) ,020901 industrial engineering & automation ,Kernel (image processing) ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Algorithm - Abstract
This paper introduces dynamic kernel convolutional neural networks (DK-CNNs), an enhanced type of CNN, by performing line-by-line scanning regular convolution to generate a latent dimension of kernel weights. The proposed DK-CNN applies regular convolution to the DK weights, which rely on a latent variable, and discretizes the space of the latent variable to extend a new dimension; this process is named “DK convolution”. DK convolution increases the expressive capacity of the convolution operation without increasing the number of parameters by searching for useful patterns within the new extended dimension. In contrast to conventional convolution, which applies a fixed kernel to analyse the changed features, DK convolution employs a DK to analyse fixed features. In addition, DK convolution can replace a standard convolution layer in any CNN network structure. The proposed DK-CNNs were compared with different network structures with and without a latent dimension on the CIFAR and FashionMNIST datasets. The experimental results show that DK-CNNs can achieve better performance than regular CNNs.
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28. Detecting spammers on social networks
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Zhipeng Zeng, Chunming Rong, Zheyi Chen, Yuanlong Yu, and Xianghan Zheng
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Social network ,Support vector machine ,business.industry ,Computer science ,media_common.quotation_subject ,Cognitive Neuroscience ,Internet privacy ,Spammer ,Construct (python library) ,Popularity ,Spamming ,Computer Science Applications ,Set (abstract data type) ,World Wide Web ,Artificial Intelligence ,Reading (process) ,Machine learning ,Feature (machine learning) ,business ,media_common - Abstract
Social network has become a very popular way for internet users to communicate and interact online. Users spend plenty of time on famous social networks (e.g., Facebook, Twitter, Sina Weibo, etc.), reading news, discussing events and posting messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behavior (e.g., post messages containing commercial URLs, following a larger amount of users, etc.), leading to great misunderstanding and inconvenience on users׳ social activities. In this paper, a supervised machine learning based solution is proposed for an effective spammer detection. The main procedure of the work is: first, collect a dataset from Sina Weibo including 30,116 users and more than 16 million messages. Then, construct a labeled dataset of users and manually classify users into spammers and non-spammers. Afterwards, extract a set of feature from message content and users׳ social behavior, and apply into SVM (Support Vector Machines) based spammer detection algorithm. The experiment shows that the proposed solution is capable to provide excellent performance with true positive rate of spammers and non-spammers reaching 99.1% and 99.9% respectively.
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