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Considering that most popular models solving aspect-level sentiment classification problems mainly focus on designing complicated neural networks to scale the importance of each word in the sentence, this paper addresses this problem from the view of semantic space. Motivated by the fact that the senses of a word can be sophisticatedly embedded into the semantic space using a distributed representation, this paper hypothesizes that each sense of a word can be represented by one or more specific dimensions, and thus the target of aspect-level sentiment classification can be simplified into searching the related dimensions for the aspects and sentiments concerned. Particularly, an Attention Vector (ATV) based on attention mechanism is designed for each aspect in terms of a specific task, which involves two sub-vectors, i.e., a Dimension Attention Vector (DATV) and a Sentiment Attention Vector (SATV). The DATV determines the significances of different dimensions based on their correlations with an aspect ; and the SATV allocates weights for the attributes of words, which are decided by sentiment polarities and part-of-speech (PoS) tagging. Given a sub-dataset related to an aspect , the ATV will be optimized by an Artificial Bee Colony (ABC) algorithm with a Support Vector Machine (SVM) classifier, the objective of which is to maximize classification accuracy. Intrinsically, the DATV can reduce the ambiguity existing in polysemy, meanwhile, the SATV is an auxiliary means for the optimization of the DATV , which can help eliminate the misunderstandings caused by antonyms. Then, the optimized DATV will be applied on a Convolutional Neural Network (CNN) model via simply scaling the pretrained word embeddings as inputs (named as ATV-CNN model). Experimental results show that the ATV-CNN model can have substantial advantages when compared with state-of-the-art models. [ABSTRACT FROM AUTHOR]

Nonparallel support vector machine is a binary classifier which has better sparsity and classification performance than other support vector machine models. However, it works slowly. Although sample selection is an effective acceleration approach, it will disturb the accuracy. Fortunately, another recently emerging screening method could speedup the solving process and keeps the accuracy unchanged. Motivated by the above research, a novel two-stage hybrid screening rule based on variational inequality and duality gap is proposed in this paper. It can reduce the scale of dual problem by deleting more redundant samples. Then, a better acceleration effect will be obtained. Meanwhile, it is still a safe method which means that the optimal solution remains unchanged. Moreover, Shrinking technique is embedded into the fast iterative algorithm DCDM to get further speedup. Thus the hybrid method can reduce the size of the problem, and accelerate the solving process greatly. Numerical experiments on ten benchmark data sets and a real Chinese red wine data set are conducted to verify the effectiveness of our hybrid acceleration strategy. [ABSTRACT FROM AUTHOR]

Recently, the research on semi-supervised support vector machine (S 3 VM) has received much attention, and many S 3 VM algorithms have been proposed. Existing studies have shown that S 3 VM is effective especially in the situations where labeled data is scarce. Nevertheless, most of existing S 3 VM algorithms belong to centralized learning, that is, all the data is stored and processed at a fusion center. In many real-world applications, data may be horizontally or vertically distributed over multiple nodes (parties). Besides, from the concerns of privacy and security, each node would not like to share its original data with the others. On the other hand, considering that the data is usually sequentially generated, online processing is preferred. In this paper, we propose two online distributed S 3 VM (dS 3 VM) algorithms, which are respectively used for horizontally and vertically partitioned data classification. In these two algorithms, to get a fully decentralized implementation, we propose a new form of manifold regularization defined on some anchor points that are adaptively selected by an online strategy. Besides, we use the sparse random feature map to approximate the kernel feature map. In this manner, the model parameters can be collaboratively estimated without transmitting the original data between neighbors. The convergence performances of the proposed algorithms are analyzed. Simulations on several data sets are performed. Results show that the proposed dS 3 VM algorithms achieve good classification performance even when there is only a small portion of labeled data. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an approach to aircraft target recognition using Inverse Synthetic Aperture Radar (ISAR) images. The goal of this work is to develop a robust algorithm to add Automated Target Recognition (ATR) capabilities to extract efficient feature vectors and ensure a comprehensive recognition process. In the first part on this paper, we used a database of ISAR images reconstructed from anechoic chamber simulations in order to extract efficient feature vectors. In the second part of this work, we proposed a recognition architecture to perform recognition tasks and provide a human operator with useful information for target recognition tasks. Several kinds of descriptor can be used to acquire information about target characteristics from radar signals and images. Indeed, a number of methods are currently used in automatic target recognition; notably based on ISAR imaging. However, target characteristic extraction from radar echoes remains a difficult task, and the methods are generally specific to either aircraft or ship recognition. In this work, we describe our approach to designing faster, more effective retrieval systems and a comprehensive architecture. Our approach uses global feature vectors for both ship and aircraft recognition. Firstly, the global feature vectors are described by two types of descriptor which have shown to be efficient in radar target recognition. The first type defines target shape obtained by watershed transformation, facilitating interpretation for the human operator. The second type of vector descriptor is based on so-called polar signatures, obtained using the polar mapping procedure. The latter are highly discriminative and thus significantly improve and facilitate target recognition, leading to greater precision and accuracy. Secondly, we describe the retrieval architecture suitable for the second type of vector descriptor, which checks invariance in relation to target rotation and scale. It is, in itself, more efficient and processing time at the retrieval step is reduced and controlled by the human operator. Finally, in order to validate our proposed feature vectors and architecture, the Support Vector Machine (SVM) classifier will be implemented; the results of which will be tested and presented in the last section of this paper. [Copyright &y& Elsevier]

Abstract: This paper proposes a classification method that is based on easily interpretable fuzzy rules and fully capitalizes on the two key technologies, namely pruning the outliers in the training data by SVMs (support vector machines), i.e., eliminating the influence of outliers on the learning process; finding a fuzzy set with sound linguistic interpretation to describe each class based on AFS (axiomatic fuzzy set) theory. Compared with other fuzzy rule-based methods, the proposed models are usually more compact and easily understandable for the users since each class is described by much fewer rules. The proposed method also comes with two other advantages, namely, each rule obtained from the proposed algorithm is simply a conjunction of some linguistic terms, there are no parameters that are required to be tuned. The proposed classification method is compared with the previously published fuzzy rule-based classifiers by testing them on 16 UCI data sets. The results show that the fuzzy rule-based classifier presented in this paper, offers a compact, understandable and accurate classification scheme. A balance is achieved between the interpretability and the accuracy. [Copyright &y& Elsevier]

This paper presents a new approach to solving the optimization task that arises when L2-SVM in its primal form is considered. In particular, we propose the application of a Barzilai–Borwein (BB) update step in five variants for the classic Stochastic Gradient Descent (SGD) algorithm. The evaluation is designed to check the effectiveness of the proposed methods in large scale scenarios in terms of execution time, convergence and sensitivity to the choice of initial parameters. The obtained results are compared with those obtained for well-known linear SVM algorithms and they indicate that the level of convergence of the proposed methods is very similar to that found in the other studies. Moreover, our approach shows much lower sensitivity to the choice of initial parameters, which allows for a substantial reduction of pre-processing. [ABSTRACT FROM AUTHOR]

Abstract: This paper formulates a twin-hypersphere support vector machine (THSVM) classifier for binary recognition. Similar to the twin support vector machine (TWSVM) classifier, this THSVM determines two hyperspheres by solving two related support vector machine (SVM)-type problems, each one is smaller than the classical SVM, which makes the THSVM be more efficient than the classical SVM. In addition, the THSVM avoids the matrix inversions in its two dual quadratic programming problems (QPPs) compared with the TWSVM. By considering the characteristics of the dual QPPs of THSVM, an efficient Gilbert’s algorithm for the THSVM based on the reduced convex hull (RCH) instead of directly optimizing its pair of QPPs is further presented. Computational results on several synthetic as well as benchmark datasets indicate the significant advantages of the THSVM classifier in the computational time and test accuracy. [Copyright &y& Elsevier]

Abstract: In this paper, we make a comparative study of the effectiveness of ensemble technique for sentiment classification. The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification procedure. First, two types of feature sets are designed for sentiment classification, namely the part-of-speech based feature sets and the word-relation based feature sets. Second, three well-known text classification algorithms, namely naı¨ve Bayes, maximum entropy and support vector machines, are employed as base-classifiers for each of the feature sets. Third, three types of ensemble methods, namely the fixed combination, weighted combination and meta-classifier combination, are evaluated for three ensemble strategies. A wide range of comparative experiments are conducted on five widely-used datasets in sentiment classification. Finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification. [ABSTRACT FROM AUTHOR]