MLTSVM: a novel twin support vector machine to multi-label learning

DOI10.1016/j.patcog.2015.10.008zbMath1394.68277OpenAlexW2184222852MaRDI QIDQ1669782

Chun-Na Li, Nai-Yang Deng, Wei-Jie Chen, Yuan-Hai Shao

Publication date: 4 September 2018

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2015.10.008

zbMATH Keywords

quadratic programmingsuccessive overrelaxationsupport vector machinesmulti-label classificationtwin support vector machines

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Twin SVM with a reject option through ROC curve, Nonparallel support vector regression model and its SMO-type solver, Multi-label learning via minimax probability machine, Robust capped L1-norm twin support vector machine, Improved multi-view GEPSVM via inter-view difference maximization and intra-view agreement minimization, Insensitive stochastic gradient twin support vector machines for large scale problems, A new multi-view multi-label model with privileged information learning, An optimized twin support vector regression algorithm enhanced by ensemble empirical mode decomposition and gated recurrent unit, A three-way selective ensemble model for multi-label classification, A novel perspective on multiclass classification: regular simplex support vector machine, MLTSVM, Semi-supervised generalized eigenvalues classification, \(\ell_1\)-norm based safe semi-supervised learning, Selective label enhancement for multi-label classification based on three-way decisions

Uses Software

• LIBSVM

Cites Work

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