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ABSTRACT. Twin Parameter-margin support vector machine (TPMSVM) is a recent very powerful binary classifier. To improve its sparsity, a linear sparse TPMSVM ...
Jun 1, 2024 · Sparse Learning for Linear Twin Parameter-margin Support Vector Machine ... A Multiclass Robust Twin Parametric Margin Support Vector Machine ...
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
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Jul 1, 2010 · This paper provides a sparse learning algorithm for Support Vector Classification (SVC), called Sparse Sup-.
The support vector machine (SVM), introduced by Vapnik [40], [41], is an excellent tool for binary data classifications. The SVM learning strategy is a ...
Twin support vector machines (TWSVM) is a new machine learning method based on the theory of Sup- port Vector Machine (SVM). Unlike SVM, TWSVM would ...
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Scalable Linear Support Vector Machine for classification implemented using liblinear. Check the See Also section of LinearSVC for more comparison element.
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This paper introduces a general Bayesian framework for obtaining sparse solutions to re- gression and classification tasks utilising models linear in the ...
... machine learning, namely, ker- nel methods, maximum margin methods, convex optimization, and sparsity/support vectors. Unlike the mostly-Bayesian treatment ...
[PDF] Learning Optimally Sparse Support Vector Machines - CS.HUJI
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Abstract. We show how to train SVMs with an opti- mal guarantee on the number of support vec- tors (up to constants), and with sample com-.
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