Detecting Happiness in Human Face using Unsupervised Twin-Support Vector Machines

Kumar, Manoj Prabhakaran; Rajagopal, Manoj Kumar

doi:10.5815/ijisa.2018.08.08

Cited by 4 publications

(2 citation statements)

References 28 publications

(36 reference statements)

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“…The method of min-max normalization [15] is a linear transformation of the original data, mapping the resulting values between [0 1]. The conversion function is:xk=(xk−xmin)/(xmax−xmin), where xmax and xmin are the maximum and minimum values of xSal, and xk is the pre-processed data.…”

Section: Methodsmentioning

confidence: 99%

Prediction of pH Value by Multi-Classification in the Weizhou Island Area

Huang

Feng

Zhu

et al. 2019

Sensors

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Ocean acidification is changing the chemical environment on which marine life depends. It causes a decrease in seawater pH and changes the water quality parameters of seawater. Changes in water quality parameters may affect pH, a key indicator for assessing ocean acidification. Therefore, it is particularly important to study the correlation between pH and various water quality parameters. In this paper, several water quality parameters with potential correlation with pH are investigated, and multiple linear regression, softmax regression, and support vector machine are used to perform multi-classification. Most importantly, experimental data were collected from Weizhou Island, China. The classification results show that the pH has a strong correlation with salinity, temperature, and dissolved oxygen. The prediction accuracy of the classification is good, and the correlation with dissolved oxygen is the most significant. The prediction accuracies of the three methods for multi-classifiers based on the above three factors reach 87.01%, 87.77%, and 89.04%, respectively.

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Section: Methodsmentioning

confidence: 99%

Prediction of pH Value by Multi-Classification in the Weizhou Island Area

Huang

Feng

Zhu

et al. 2019

Sensors

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“…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. Authors [81] also proposed semisupervised multi TSVM to predict human facial emotions with 13 minimal features that can detect six basic human emotions.…”

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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