New Support Vector Algorithms
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TLDR
A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.Abstract:
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.read more
Citations
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Rapid on-site peak ground acceleration estimation based on support vector regression and P-wave features in Taiwan
Ting-Yu Hsu,Shieh-Kung Huang,Yu Weng Chang,Chun Hsiang Kuo,Che Min Lin,Tao Ming Chang,Kuo-Liang Wen,Kuo-Liang Wen,Chin-Hsiung Loh,Chin-Hsiung Loh +9 more
TL;DR: In this article, support vector regression was employed to establish a regression model which can predict the peak ground acceleration according to P-wave features from the first few seconds of vertical ground acceleration of a single station.
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Some Experimental Issues in Financial Fraud Mining
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Laplacian twin parametric-margin support vector machine for semi-supervised classification
Zhiji Yang,Yitian Xu +1 more
TL;DR: This work proposes a Laplacian twin parametric-margin support vector machine (LTPMSVM) for the semi-supervised classification, which exploits the geometric information of the marginal distribution embedded in unlabeled data to construct a more reasonable classifier.
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Modified twin support vector regression
TL;DR: The present study suggest modified twin support vector regression (MTSVR) for data regression using Particle Swarm Optimization (PSO) algorithm to determine the parameters of the MTSVR model.
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Estimation of immune cell content in tumor using single-cell RNA-seq reference data.
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A Tutorial on Support Vector Machines for Pattern Recognition
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