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It is thought to be an effective technique to handle the problem of educational data explosion and lack of information by identifying potential relationships between data and directing decision-makers through the extraction, transformation, analysis, and modeling of educational data. Based on this, this research constructs a data analysis model for education evaluation using the K-means clustering technique in DM. The weight of each index of students' comprehensive quality is calculated using AHP, and the value of the weight is used to determine whether the index is the important feature of analysis system mining. Improved sampling technology is used to deal with the representation of large-scale data sets; a sample partition clustering technique is proposed as a general framework. The best accuracy of this method, according to experimental data, is 95.6 percent, which is 12.1 percent greater than Mi cluster algorithm and 6.8 percent higher than DRCluster algorithm. The K-means clustering analysis technology is used to analyze students' comprehensive evaluation data in this paper, with the goal of determining the regularity of data implication, accurately diagnosing learning problems, and providing the foundation for developing effective student management strategies. [ABSTRACT FROM AUTHOR]

Summary: Sparse Bayesian learning (SBL) is a well‐established technique for tackling supervised learning problems, while taking advantage of the prior knowledge that the expected solution is sparse. Based on the premise that initial damage of a structure appears only in a limited number of locations, SBL has been explored for identifying structural damage, showing promising results. Existing SBL methods for structural damage identification use measurements related to modal properties and are thus limited to linear models. In this paper, we present a methodology that allows for application of SBL in nonlinear models, using time history measurements. We develop a two‐step optimization algorithm in which the most probable values of the structural model parameters and the hyperparameters are iteratively obtained. An equivalent, single‐objective, minimization problem that results in the most probable model parameter values is also derived. We consider the example problem of identifying damage in the form of weld fractures in a 15‐story moment‐resisting steel‐frame building, using a nonlinear finite‐element model and simulated acceleration data. Fiber elements and a bilinear material model are used to account for the change in local stiffness when cracks at the welds are subjected to tension, and the model parameters characterize the loss of stiffness as the cracks open under tension. The damage identification results demonstrate the effectiveness and robustness of the proposed methodology in identifying the existence, location, and severity of damage for a variety of different damage scenarios and levels of model and measurement error. [ABSTRACT FROM AUTHOR]

The nearest neighbor search is an essential operation for many computer vision, data mining, and machine learning problems. Since it is so widely used, the nearest neighbor search should be as fast as possible. This paper explores lower bound‐based approaches to speed up the exact nearest neighbor search in high dimensional Euclidean space. We compute the lower bound of Euclidean Distance by using the block vectors and Cauchy–Schwartz inequality. The proposed lower bound is calculated efficiently and is close to the real Euclidean Distance. Besides, the preprocessing step of the proposal has linear time complexity. Given a query, during the procedure of identifying the nearest neighbor, our method can eliminate many expensive actual distance computations using the lower bound to approximate Euclidean Distance. In addition, we develop a multilevel lower bound strategy, which calculates the lower bound step by step and utilizes the multistep filtering mechanism to improve the searching process further. Theoretical analysis is provided to show that the proposals can guarantee to obtain the same result as the brute‐force search. Comprehensive experiments on 16 public data sets collected from various domains demonstrate that our approach performs well in finding the exact nearest neighbor compared to related competitors. The experimental results also illustrate that the multilevel lower bound strategy is effective. [ABSTRACT FROM AUTHOR]

In multi‐agent reinforcement learning, transfer learning is one of the key techniques used to speed up learning performance through the exchange of knowledge among agents. However, there are three challenges associated with applying this technique to real‐world problems. First, most real‐world domains are partially rather than fully observable. Second, it is difficult to pre‐collect knowledge in unknown domains. Third, negative transfer impedes the learning progress. We observe that differentially private mechanisms can overcome these challenges due to their randomization property. Therefore, we propose a novel differential transfer learning method for multi‐agent reinforcement learning problems, characterized by the following three key features. First, our method allows agents to implement real‐time knowledge transfers between each other in partially observable domains. Second, our method eliminates the constraints on the relevance of transferred knowledge, which expands the knowledge set to a large extent. Third, our method improves robustness to negative transfers by applying differentially exponential noise and relevance weights to transferred knowledge. The proposed method is the first to use the randomization property of differential privacy to stimulate the learning performance in multi‐agent reinforcement learning system. We further implement extensive experiments to demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire multiple spikes in all layers. The feedforward network consists of spiking neurons governed by biologically plausible long-term memory spike response model, in which the effect of earlier spikes on the refractoriness is not neglected to incorporate adaptation effects. The gradient descent method is employed to derive synaptic weight updating rule for learning spike trains. The proposed algorithm is tested and verified on spatiotemporal pattern learning problems, including a set of spike train learning tasks and nonlinear pattern classification problems on four UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms. [ABSTRACT FROM AUTHOR]

In many real-world machine learning problems, the features are changing along the time, with some old features vanishing and some other new features augmented, while the remaining features survived. In this paper, we propose the cross-feature attention network to handle the incremental and decremental features. This network is composed of multiple cross-feature attention encoding-decoding layers. In each layer, the data samples are firstly encoded by the combination of other samples with vanished/augmented features and weighted by the attention weights calculated by the survived features. Then, the samples are encoded by the combination of samples with the survived features weighted by the attention weights calculated from the encoded vanished/augmented feature data. The encoded vanished/augmented/survived features are then decoded and fed to the next cross-feature attention layer. In this way, the incremental and decremental features are bridged by paying attention to each other, and the gap between data samples with a different set of features are filled by the attention mechanism. The outputs of the cross-feature attention network are further concatenated and fed to the class-specific attention and global attention network for the purpose of classification. We evaluate the proposed network with benchmark data sets of computer vision, IoT, and bio-informatics, with incremental and decremental features. Encouraging experimental results show the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Recently, the application of deep reinforcement learning in the recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns, and sparse data issues in the recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This study proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in the recommender system. Our experiment is carried out on the MovieLens dataset. The result shows that the DRL-based recommender system is superior than traditional algorithms in minimum error, and the application of tags have little effect on accuracy when making up for interpretability. In addition, the DRL-based recommender system has excellent performance on user cold start problems. [ABSTRACT FROM AUTHOR]

This paper discusses the predictive maintenance (PM) problem of a single equipment system. It is assumed that the equipment has deteriorating quality states as it operates, resulting in multiple yield levels represented as system observation states. We cast the equipment deterioration as discrete-state and continuous-time semi-Markov decision process (SMDP) model and solve the SMDP problem in reinforcement learning (RL) framework using the strategy-based method. In doing so, the goal is to maximize the system average reward rate (SARR) and generate the optimal maintenance strategy for given observation states. Further, the PM time is capable of being produced by a simulation method. In order to prove the advantage of our proposed method, we introduce the standard sequential preventive maintenance algorithm with unequal time interval. Our proposed method is compared with the sequential preventive maintenance algorithm in a test objective of SARR, and the results tell us that our proposed method can outperform the sequential preventive maintenance algorithm. In the end, the sensitivity analysis of some parameters on the PM time is given. [ABSTRACT FROM AUTHOR]

In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting. The Tikhonov regularization method is widely used to solve complex problems in engineering. The vertical derivative of gravity can highlight the local anomalies and separate the horizontal superimposed abnormal bodies. The higher the order of the vertical derivative is, the stronger the resolution is. However, it is generally considered that the conversion of the high-order vertical derivative is unstable. In this paper, based on Tikhonov regularization for solving the high-order vertical derivatives of gravity field and combining with the iterative method for successive approximation, the Tikhonov regularization method for calculating the vertical high-order derivative in gravity field is proposed. The recurrence formula of Tikhonov regularization iterative method is obtained. Through the analysis of the filtering characteristics of this method, the high-order vertical derivative of gravity field calculated by this method is stable. Model tests and practical data processing also show that the method is of important theoretical significance and practical value. [ABSTRACT FROM AUTHOR]

With the rapid development of big data, artificial intelligence teaching systems have gradually been developed extensively. The powerful artificial intelligence teaching systems have become a tool for teachers and students to learn independently in various universities. The characteristic of artificial intelligence teaching system is to get rid of the constraints of traditional teaching time and space and build a brand-new learning environment, which is the mainstream trend of future learning. As the carrier of students' autonomous learning, the artificial intelligence teaching system provides a wealth of learning resources and learning tools on the one hand, and on the other hand, it gradually accumulates more and more learning behaviors, learning status, and other large amounts of data, which is an in-depth study of online learning and provides valuable and generative dynamic resources. Based on relevant researches on domestic and foreign related learning analysis and common big data analysis methods, combined with actual learning evaluation goals, this paper proposes an artificial intelligence teaching system using big data analysis methods and a modeling process framework for online learning evaluation and uses student data to carry out predictive evaluation modeling to evaluate student learning outcomes. The evaluation results can enable teachers to predict whether students can successfully complete the course of learning after a period of teaching. Through the final evaluation, students' learning problems can be discovered in time based on the evaluation results, and targeted interventions can be made for students who are at risk. The scientific and objective learning evaluation obtained in this study through data analysis can not only provide teachers with relevant information and provide personalized guidance to students, but also improve the adaptive and personalized service functions of the learning platform of the artificial intelligence teaching system, greatly reducing teachers teaching burden. Artificial intelligence teaching evaluation can help educators understand the problems in teaching, adjust teaching strategies in time, and improve teaching results. [ABSTRACT FROM AUTHOR]

The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency. [ABSTRACT FROM AUTHOR]

This paper deals with a single-machine resource allocation scheduling problem with learning effect and group technology. Under slack due-date assignment, our objective is to determine the optimal sequence of jobs and groups, optimal due-date assignment, and optimal resource allocation such that the weighted sum of earliness and tardiness penalties, common flow allowances, and resource consumption cost is minimized. For three special cases, it is proved that the problem can be solved in polynomial time. To solve the general case of problem, the heuristic, tabu search, and branch-and-bound algorithms are proposed. [ABSTRACT FROM AUTHOR]

It can be challenging to learn algorithms due to the research of business-related few-shot classification problems. Therefore, in this paper, we evaluate the classification of few-shot learning in the commercial field. To accurately identify the categories of few-shot learning problems, we proposed a probabilistic network (PN) method based on few-shot and one-shot learning problems. The enhancement of the original data was followed by the subsequent development of the PN method based on feature extraction, category comparison, and loss function analysis. The effectiveness of the method was validated using two examples (absenteeism at work and Las Vegas Strip hotels). Experimental results demonstrate the ability of the PN method to effectively identify the categories of commercial few-shot learning problems. Therefore, the proposed method can be applied to business-related few-shot classification problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel semisupervised learning framework to learn the flows of edges over a graph. Given the flow values of the labeled edges, the task of this paper is to learn the unknown flow values of the remaining unlabeled edges. To this end, we introduce a value amount hold by each node and impose that the amount of values flowing from the conjunctive edges of each node to be consistent with the node's own value. We propose to embed the nodes to a continuous vector space so that the embedding vector of each node can be reconstructed from its neighbors by a recursive neural network model, linear normalized long short-term memory. Moreover, we argue that the value of each node is also embedded in the embedding vectors of its neighbors, thus propose to approximate the node value from the output of the neighborhood recursive network. We build a unified learning framework by formulating a minimization problem. To construct the learning problem, we build three subproblems of minimization: (1) the embedding error of each node from the recursive network, (2) the loss of the construction for the amount of value of each node, and (3) the difference between the value amount of each node and the estimated value from the edge flows. We develop an iterative algorithm to learn the node embeddings, edge flows, and node values jointly. We perform experiments based on the datasets of some network data, including the transportation network and innovation. The experimental results indicate that our algorithm is more effective than the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Proximal point algorithm is a type of method widely used in solving optimization problems and some practical problems such as machine learning in recent years. In this paper, a framework of accelerated proximal point algorithm is presented for convex minimization with linear constraints. The algorithm can be seen as an extension to G u ¨ ler's methods for unconstrained optimization and linear programming problems. We prove that the sequence generated by the algorithm converges to a KKT solution of the original problem under appropriate conditions with the convergence rate of O 1 / k 2 . [ABSTRACT FROM AUTHOR]

The joint feature selection problem can be resolved by solving a matrix l 2,1 -norm minimization problem. For l 2,1 -norm regularization, one of the most fascinating features is that some similar sparsity structures can be employed by multiple predictors. However, the nonsmooth nature of the problem brings great challenges to the problem. In this paper, an alternating direction multiplier method combined with the spectral gradient method is proposed for solving the matrix l 2,1 -norm optimization problem involved with multitask feature learning. Numerical experiments show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose to embed a content-rich network for the purpose of similarity searching for a query node. In this network, besides the information of the nodes and edges, we also have the content of each node. We use the convolutional neural network (CNN) to represent the content of each node and then use the graph convolutional network (GCN) to further represent the node by merging the representations of its neighboring nodes. The GCN output is further fed to a deep encoder-decoder model to convert each node to a Gaussian distribution and then convert back to its node identity. The dissimilarity between the two nodes is measured by the Wasserstein distance between their Gaussian distributions. We define the nodes of the network to be positives if they are relevant to the query node and negative if they are irrelevant. The labeling of the positives/negatives is based on an upper bound and a lower bound of the Wasserstein distances between the candidate nodes and the query nodes. We learn the parameters of CNN, GCN, encoder-decoder model, Gaussian distributions, and the upper bound and lower bounds jointly. The learning problem is modeled as a minimization problem to minimize the losses of node identification, network structure preservation, positive/negative query-specific relevance-guild distance, and model complexity. An iterative algorithm is developed to solve the minimization problem. We conducted experiments over benchmark networks, especially innovation networks, to verify the effectiveness of the proposed method and showed its advantage over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The discrete Sugeno integral is an aggregation function particularly suited to aggregation of ordinal inputs. It accounts for inputs interactions, such as redundancy and complementarity, and its learning from empirical data is a challenging optimisation problem. The methods of ordinal regression involve an expensive objective function, whose complexity is quadratic in the number of data. We formulate ordinal regression using a much less expensive objective computed in linear time by the pool‐adjacent‐violators algorithm. We investigate the learning problem numerically and show the superiority of the new algorithm. [ABSTRACT FROM AUTHOR]

Imbalanced data sets are a common occurrence in important machine learning problems. Research in improving learning under imbalanced conditions has largely focused on classification problems (ie, problems with a categorical dependent variable). However, imbalanced data also occur in function approximation, and far less attention has been paid to this case. We present a novel stratification approach for imbalanced function approximation problems. Our solution extends the SMOTE oversampling preprocessing technique to continuous‐valued dependent variables by identifying regions of the feature space with a low density of examples and high variance in the dependent variable. Synthetic examples are then generated between nearest neighbors in these regions. In an empirical validation, our approach reduces the normalized mean‐squared prediction error in 18 out of 21 benchmark data sets, and compares favorably with state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multitask learning method based on the deep convolutional network. The proposed deep network has four convolutional layers, three max-pooling layers, and two parallel fully connected layers. To adjust the deep network to multitask learning problem, we propose to learn a low-rank deep network so that the relation among different tasks can be explored. We proposed to minimize the number of independent parameter rows of one fully connected layer to explore the relations among different tasks, which is measured by the nuclear norm of the parameter of one fully connected layer, and seek a low-rank parameter matrix. Meanwhile, we also propose to regularize another fully connected layer by sparsity penalty so that the useful features learned by the lower layers can be selected. The learning problem is solved by an iterative algorithm based on gradient descent and back-propagation algorithms. The proposed algorithm is evaluated over benchmark datasets of multiple face attribute prediction, multitask natural language processing, and joint economics index predictions. The evaluation results show the advantage of the low-rank deep CNN model over multitask problems. [ABSTRACT FROM AUTHOR]

In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28) and Uyghur-Chinese (+0.33) MT. [ABSTRACT FROM AUTHOR]

In the machine learning based on kernel tricks, people often put one variable of a kernel function on the given samples to produce the basic functions of a solution space of learning problem. If the collection of the given samples deviates from the data distribution, the solution space spanned by these basic functions will also deviate from the real solution space of learning problem. In this paper a multikernel-like learning algorithm based on data probability distribution (MKDPD) is proposed, in which the parameters of a kernel function are locally adjusted according to the data probability distribution, and thus produces different kernel functions. These different kernel functions will generate different Reproducing Kernel Hilbert Spaces (RKHS). The direct sum of the subspaces of these RKHS constitutes the solution space of learning problem. Furthermore, based on the proposed MKDPD algorithm, a new algorithm for labeling new coming data is proposed, in which the basic functions are retrained according to the new coming data, while the coefficients of the retrained basic functions remained unchanged to label the new coming data. The experimental results presented in this paper show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In 2012, Moraglio and coauthors introduced new genetic operators for Genetic Programming, called geometric semantic genetic operators. They have the very interesting advantage of inducing a unimodal error surface for any supervised learning problem. At the same time, they have the important drawback of generating very large data models that are usually very hard to understand and interpret. The objective of this work is to alleviate this drawback, still maintaining the advantage. More in particular, we propose an elitist version of geometric semantic operators, in which offspring are accepted in the new population only if they have better fitness than their parents. We present experimental evidence, on five complex real-life test problems, that this simple idea allows us to obtain results of a comparable quality (in terms of fitness), but with much smaller data models, compared to the standard geometric semantic operators. In the final part of the paper, we also explain the reason why we consider this a significant improvement, showing that the proposed elitist operators generate manageable models, while the models generated by the standard operators are so large in size that they can be considered unmanageable. [ABSTRACT FROM AUTHOR]

Feature selection has proved to be a beneficial tool in learning problems with the main advantages of interpretation and generalization. Most existing feature selection methods do not achieve optimal classification performance, since they neglect the correlations among highly correlated features which all contribute to classification. In this paper, a novel semisupervised feature selection algorithm based on support vector machine (SVM) is proposed, termed SENFS. In order to solve SENFS, an efficient algorithm based on the alternating direction method of multipliers is then developed. One advantage of SENFS is that it encourages highly correlated features to be selected or removed together. Experimental results demonstrate the effectiveness of our feature selection method on simulation data and benchmark data sets. [ABSTRACT FROM AUTHOR]

PURPOSE. 22q11 deletion syndrome (22q11DS) is a very common, but commonly overlooked, disorder that has many viariable features, such as serious heart defects, speech and articulation problems, immune compromise, learning problems, pervasive developmental disorders, and a late appearing phenotype of this condition—mental illness. CONCLUSION. Persons with 22q11DS are likely to be medically complex patients with pervasive developmental disorders and late appearing mental problems. PRACTICE IMPLICATIONS. Establishing this diagnosis is useful to families and healthcare providers to anticipate and address immediate and future issues surrounding health promotion, disease prevention, and health maintenence. [ABSTRACT FROM AUTHOR]