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https://www.sciencedirect.com/science/article/pii/S0957417415005679
Sparse Proximal Support Vector Machines is an embedded feature selection method. • sPSVMs removes more than 98% of features in many high dimensional datasets. • An efficient alternating optimization technique is proposed. • sPSVMs induces class-specific local sparsity.Cited by: 11
https://research.cs.wisc.edu/dmi/svm/psvm/
Iinstead of a standard support vector machine that classifies points by assigning them to one of two disjoint half-spaces, PSVM classifies points by assigning them to the closest of two parallel planes. For more information, see our paper Proximal Support Vector Machines. SVMs are an optimization based approach for solving machine learning ...
http://support-vector-machines.org/SVM_soft.html
SVM, support vector machines, SVMC, support vector machines classification, SVMR, support vector machines regression, kernel, machine learning, pattern recognition, cheminformatics, computational chemistry, bioinformatics, computational biology ... PSVM (Proximal Support Vector Machine) is a MATLAB script by Fung and Mangasarian which ...
https://ieeexplore.ieee.org/document/8249754/
Abstract: Generalized eigenvalue proximal support vector machines (GEPSVMs) are a simple and effective binary classification method in which each hyperplane is closest to one of the two classes and as far as possible from the other class. They solve a pair of generalized eigenvalue problems to obtain two nonparallel hyperplanes. Multiview learning considers learning with multiple feature sets ...Cited by: 9
https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/ListenSemester1_2009_10/p77-fung.pdf
Section 2 we introduce the proximal linear support vector machine, give the Linear Proximal Algorithm 2.1 and an ex- plicit expression for the leave-one-out-correctness in terms of problem data (16). In Section 3 we introduce the proximal kernel-based nonlinear support vector machine, the corre-
https://dl.acm.org/citation.cfm?id=502527
Instead of a standard support vector machine (SVM) that classifies points by assigning them to one of two disjoint half-spaces, points are classified by assigning them to the closest of two parallel planes (in input or feature space) that are pushed apart as far as possible.Cited by: 1063
https://www.cs.iastate.edu/~honavar/proximal-svm.pdf
algorithm, a k-category proximal support vector machine (PSVM) classifier. Proximal support vector machines and related approaches (Fung & Mangasarian, 2001; Suykens & Vandewalle, 1999) can be interpreted as ridge regression applied to classification problems (Evgeniou, Pontil, & Poggio, 2000). Extensive computational
https://dl.acm.org/doi/10.1145/502512.502527
Instead of a standard support vector machine (SVM) that classifies points by assigning them to one of two disjoint half-spaces, points are classified by assigning them to the closest of two parallel planes (in input or feature space) that are pushed apart as far as possible.
https://www.sciencedirect.com/science/article/pii/S0031320315003672
Thus, the proposed approach will be naturally named: one class proximal support vector machines. Lastly, the proposed method is tested and compared with recent novelty detectors. 2. Notations. Consider a binary classification problem with a training dataset S of size n defined as: S = {(x i, u i) / x i …Cited by: 14
http://ftp.cs.wisc.edu/pub/dmi/tech-reports/01-02.pdf
The linear proximal SVM can easily handle large datasets as indicated by the classi ca-tion of a 2 million point 10-attribute set in 20.8 seconds. All computational results are based on 6 lines of MATLAB code. Keywords data classi cation, support vector machines, linear equa-tions 1. INTRODUCTION Standard support vector machines (SVMs) [36, 6 ...
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