Efficient sparse least squares support vector machines for pattern classification

DOI10.1016/J.CAMWA.2013.06.028zbMath1381.68252OpenAlexW2164578338MaRDI QIDQ316308

Zhiquan Qi, Yong Shi, Ying-Jie Tian, Xu-Chan Ju

Publication date: 27 September 2016

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2013.06.028

zbMATH Keywords

classificationloss functionregressionleast squares support vector machinesparseness

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Uses Software

• UCI-ml
• LIBSVM
• mGene

Cites Work

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• Benchmarking least squares support vector machine classifiers
• Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
• Model selection for the LS-SVM. Application to handwriting recognition
• Weighted least squares support vector machines: robustness and sparse approximation
• Improved sparse least-squares support vector machines
• Support-vector networks
• Optimized fixed-size kernel models for large data sets
• SMO Algorithm for Least-Squares SVM Formulations
• Efficient computations for large least square support vector machine classifiers
• Leave-One-Out Bounds for Support Vector Regression Model Selection
• Multicategory proximal support vector machine classifiers

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