A coordinate descent margin based-twin support vector machine for classification

DOI10.1016/J.NEUNET.2011.08.003zbMath1259.68173DBLPjournals/nn/ShaoD12OpenAlexW1999466278WikidataQ44977337 ScholiaQ44977337MaRDI QIDQ1943043

Yuan-Hai Shao, Nai-Yang Deng

Publication date: 15 March 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2011.08.003

zbMATH Keywords

pattern classificationmaximum margintwin support vector machinescoordinate descent method

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (7)

Incremental and decremental fuzzy bounded twin support vector machine ⋮ Multi-view L2-SVM and its multi-view core vector machine ⋮ Class imbalance learning using fuzzy ART and intuitionistic fuzzy twin support vector machines ⋮ Insensitive stochastic gradient twin support vector machines for large scale problems ⋮ Nonparallel hyperplane support vector machine for binary classification problems ⋮ Improved twin support vector machine ⋮ A GA-based model selection for smooth twin parametric-margin support vector machine

Uses Software

• UCI-ml
• SVMlight
• LIBSVM
• LIBLINEAR
• RSVM
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Cites Work

• Optimal kernel selection in twin support vector machines
• Error bounds and convergence analysis of feasible descent methods: A general approach
• Support-vector networks
• The new interpretation of support vector machines on statistical learning theory
• Chaos control using least-squares support vector machines
• Multicategory proximal support vector machine classifiers
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

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