Multi-label Lagrangian support vector machine with random block coordinate descent method

DOI10.1016/j.ins.2015.09.023zbMath1390.68561OpenAlexW2211186796MaRDI QIDQ1750531

Jianhua Xu

Publication date: 22 May 2018

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2015.09.023

zbMATH Keywords

quadratic programmingkernel functionblock coordinate descent methodsupport vector machinemulti-label classification

Mathematics Subject Classification ID

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

Related Items (1)

Multi-label optimal margin distribution machine

Uses Software

• MULAN
• BoosTexter
• ML-KNN

Cites Work

• On label dependence and loss minimization in multi-label classification
• A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints
• Feature selection for multi-label naive Bayes classification
• ML-KNN: A lazy learning approach to multi-label learning
• Multilabel classification via calibrated label ranking
• BoosTexter: A boosting-based system for text categorization
• Fast multi-label core vector machine
• Combining instance-based learning and logistic regression for multilabel classification
• Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
• Block Coordinate Descent Methods for Semidefinite Programming
• Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems
• Some comments on Wolfe's ‘away step’
• 10.1162/15324430152748218
• Matrix Algebra
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