Action Recognition by Local Space-Time Features and Least Square Twin SVM (LS-TSVM)

Mozafari, Kourosh; Nasiri, Jalal A.; Charkari, Nasrollah Moghadam; Jalili, Saeed

doi:10.1109/ici.2011.55

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…Khemchandani and Sharma [74] also proposed robust parametric twin support vector machine which can effectively deal with the noise. Mozafari et al [95] used the Harris detector algorithm and applied LS-TSVM for action recognition and achieved the highest accuracy than other state-of-the-art methods. Kumar and Rajagopal [80] proposed Multi-class TSVM for detecting human face happiness combined with Constrained Local Model.…”

Section: Applications Of Twin Support Vector Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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Section: Applications Of Twin Support Vector Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…[152] addressed the task of gesture recognition using surface electromyogram (sEMG) data, and proved TWSVM to be an effective approach in meeting the learning problem from multi-class data where patterns in different classes arise from different distributions, since TWSVM is a more natural choice for applying to unbalanced datasets. Human activity recognition (HAR) is an important research branch in computer vision, and [153] proposed a framework for HAR with the combination of local space-time features and LSTWSVM. Speech emotion recognition is used to solve the problem of "how to speak", just like speaker recognition is proposed to solve "who is speaking", TWSVM was applied into this problem tentatively [154].…”

Section: Applications Of Twsvmsmentioning

confidence: 99%

Review on: Twin Support Vector Machines

Tian

2014

Ann. Data. Sci.

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Twin support vector machine (TWSVM), an useful extension of the traditional SVM, becomes the current researching hot spot in machine learning during the last few years. For the binary classification problem, the basic idea of TWSVM is to seek two nonparallel proximal hyperplanes such that each hyperplane is closer to one of the two classes and is at least one distance from the other. TWSVM has lower computational complexity and better generalization ability, therefore in the last few years it has been studied extensively and developed rapidly. Considering the many variants of TWSVM, a systematic survey is needed and helpful to understand and use this family of data mining techniques more easily. The purpose of this paper is to closely review TWSVMs and provide an insightful understanding of current developments, at the same time point out their limitations and highlight the major opportunities and challenges, as well as potential important research directions.

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