Weighted linear loss multiple birth support vector machine based on information granulation for multi-class classification

Ding, Shifei; Zhang, Xiekai; An, Yuexuan; Xue, Yu

doi:10.1016/j.patcog.2017.02.011

Cited by 72 publications

(20 citation statements)

References 45 publications

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“…Thus, parameter selection is a practical problem and should be investigated in the future. Moreover, the extension of the proposed EFTBSVM to multi-class [66]- [68], multi-label [69], [70] and multi-view [71], [72] classification problems are also interesting.…”

Section: Discussionmentioning

confidence: 99%

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

et al. 2019

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Twin support vector machine (TWSVM) is a new machine learning method, as opposed to solving a single quadratic programming problem in support vector machine (SVM), which generates two nonparallel hyperplanes by solving two smaller size quadratic programming problems. However, the TWSVM obtains the final classifier by giving the same importance to all training samples which may be important for classification performance. In order to address this problem, in this paper, we propose a novel entropy-based fuzzy twin bounded support vector machine (EFTBSVM) for binary classification problems. By considering the fuzzy membership value for each sample and assigning it based on the entropy value, the samples with higher class certainty are assigned to relatively larger fuzzy membership. In addition, the proposed EFTBSVM not only maintains the superior characteristics of the TWSVM but also exploits the structural risk minimization principle by introducing a regularization term. The experimental results achieved on synthetic datasets and benchmark datasets illustrate the effectiveness of the proposed method.

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Section: Discussionmentioning

confidence: 99%

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

et al. 2019

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“…It should be pointed out that there are many parameters in our WLPTSVM, so parameter selection is a practical problem and needs to be investigated in the future. In addition, the extension of our WLPTSVM to multiclass classification [46]- [48], multi-label classification [49] and feature selection problems [50], [51] are also interesting. Furthermore, how to use our WLPTSVM to deal with the large-scale classification problems in real world is also under our consideration.…”

Section: Discussionmentioning

confidence: 99%

Weighted Linear Loss Projection Twin Support Vector Machine for Pattern Classification

2019

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Based on the recently proposed projection twin support vector machine (PTSVM) and least squares projection twin support vector machine (LSPTSVM), in this paper, we propose a weighted linear loss projection twin support vector machine, namely WLPTSVM for short. By introducing the weighted linear loss function, the proposed WLPTSVM not only solves systems of linear equations with lower computational cost but also obtains comparable classification accuracy. In addition, it is able to dispose of large scale classification problems efficiently without any extra external optimizers. The experiments conducted on synthetic and several benchmark datasets illustrate the effectiveness of our WLPTSVM.INDEX TERMS Pattern classification, twin support vector machine, projection twin support vector machine, weighted linear loss function.

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“…Support Vector Machines were introduced [16] as a technique aimed to solve binary classification problems; due to their solid theoretical fundaments, SVMs have been used to answer regression, clustering and multi-classification tasks [17] along with practical applications in several fields, including computer vision [18], text classification [19], and natural language processing [20], among others. It can be defined as a discriminative classifier which works with labelled training data to output an optimal hyperplane used to categorize new examples.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Emotion Recognition in Video with OpenCV and Cognitive Services API: A Comparison

Prieto¹,

Oplatková²

2017

DAAAM Proceedings

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This Publication has to be referred as: Beltran Prieto, L[uis] A[ntonio] & Kominkova Oplatkova, Z[uzana] (2017). Emotion Recognition in Video with AbstractEmotions are people's reactions to certain stimuli. Most common way to detect an emotion is by facial expression analysis. Machine learning algorithms combined with other artificial intelligence techniques have been developed in order to identify expressions found in images and videos. Support Vector Machines, along with Haar Cascade classifiers can be used for efficient emotion recognition. OpenCV, an open-source library for machine learning, makes it possible to develop computer-vision applications. Cognitive Services is a free set of APIs which easily integrate artificial intelligence in applications. In this paper a comparison between two implementations of Emotion Recognition algorithms, namely SVM and Cognitive Services API, was carried out to compare their performance. For this research, 500 tests were performed per experiment. The SVM implementation in OpenCV obtained the best performance, with an 84% accuracy, which can be boosted by increasing the sample size per emotion.

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Weighted linear loss multiple birth support vector machine based on information granulation for multi-class classification

Cited by 72 publications

References 45 publications

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

Weighted Linear Loss Projection Twin Support Vector Machine for Pattern Classification

Emotion Recognition in Video with OpenCV and Cognitive Services API: A Comparison

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