A Fuzzy Least Squares Support Tensor Machines in Machine Learning

Zhang, Ruiting; Zhou, Zhijian

doi:10.3991/ijet.v10i8.5203

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Chen et al addressed one-class classification problem with tensor-based maximal margin classification paradigm and formulated One-Class Support Tensor Machine (OCSTM) [12]. In order to utilize the structural information present in high dimensional features, tensor based algorithm Fuzzy Least Squares Support Tensor Machine (FLSSTM) is proposed by Zhang et al [13] Kotsia et al formulated the higher rank STMs in which the separating hyperplane is defined by the parameters and constrained to be the sum of rank one tensors [14]. Zhang et al generalized vector-based TSVM to tensor-based algorithm TSTM and the proposed algorithm were applied for micro calcification clusters detection [15].…”

Section: Introductionmentioning

confidence: 99%

A v -Twin Support Tensor Machine

Wang¹,

Wu²,

Zhou³

2016

dtetr

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The traditional vector-based algorithms, such as Support Vector Machine (SVM) and Twin Support Vector Machine (TSVM), have many limitations especially when tensor is considered as input matrix. In this paper, we proposed a novel algorithm with a tensor-based classification paradigm to utilize the structural information. The new proposed algorithm is called  -Twin Support Tensor Machine (  -TSTM), which is an extension of  -Twin Support Vector Machine (  -TSVM). Similarly,  -TSTM solves a pair of smaller-sized Quadratic Programming Problems (QPPs). It reduces the computational complexity substantially. Besides, we formulate  -TSTM, which separates samples in the tensor space with two non-parallel hyperplanes, and the pair of parameters ( ) have theoretical interpretation which are used to control the bounds of the fractions of support tensors and the error margins. What's more, the structure information of data is retained by the direct use of tensor representation. The proposed  -TSTM can preferably overcome overfitting problem and deal with big data while most vector-based algorithms could hardly compare. In addition, it has better performances on high dimensional and small-sample-size (S3) problem. The efficiency and superiority of the proposed method are demonstrated by experiments on various datasets.

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Section: Introductionmentioning

confidence: 99%

A v -Twin Support Tensor Machine

Wang¹,

Wu²,

Zhou³

2016

dtetr

Self Cite

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“…Under the help of CAT, translators do not need traditional dictionary or specialized book for reference, and they just key in the content into the translation program and the draft translation product will appear on their computers screen, as stated in Ref. [2]. CAT, faster and sometimes more accurate compared with the slow and expensive human-based translation, has aroused great interest among researchers, and the huge potential of CAT teaching in translation class has been recognized by many language teachers.…”

Section: Introductionmentioning

confidence: 99%

Application of Computer-aided Translation in English Teaching

Yao

2017

Int. J. Emerg. Technol. Learn.

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Abstract-The continuous development of computer information technology has brought about many changes in people's life. At present, the computeraided translation technology (abbreviated as CAT hereafter), being a kind of important auxiliary method, has been widely used in software localization and the translation of scientific works. This realistic need has aroused more and more attention to the development and use of CAT tools. Using the computer to assist teaching was first initiated in the USA in the 1950's and then widely spread all over the world. Until recently, an increasing number of teachers get to know its great use, and try to apply it to the process of teaching. In China the application of CAT in translation teaching is still at the initial stage. In this paper, the author focuses on the present situation of the adoption of CAT technology in translation teaching in China. This paper conducted a survey to investigate people's understanding over it from teachers' perspective and existing problems in its adoption in translation teaching.

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Application of support higher-order tensor machine in fault diagnosis of electric vehicle range-extender

Zhang

et al. 2017

2017 Chinese Automation Congress (CAC)

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A Fuzzy Least Squares Support Tensor Machines in Machine Learning

Cited by 3 publications

References 13 publications

A v -Twin Support Tensor Machine

A v -Twin Support Tensor Machine

Application of Computer-aided Translation in English Teaching

Application of support higher-order tensor machine in fault diagnosis of electric vehicle range-extender

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