1-norm support vector novelty detection and its sparseness

DOI10.1016/j.neunet.2013.07.010zbMath1297.68209OpenAlexW2038829083WikidataQ50724777 ScholiaQ50724777MaRDI QIDQ460681

Li Zhang, Wei-Da Zhou

Publication date: 14 October 2014

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2013.07.010

zbMATH Keywords

linear programming1-norm regularizationsupport vector novelty detection

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Linear programming (90C05)

Related Items (1)

A new robust model of one-class classification by interval-valued training data using the triangular kernel

Uses Software

• UCI-ml
• GLPK
• LIBSVM
• StatLib Datasets Archive

Cites Work

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• Novelty detection: a review. I. Statistical approaches
• Novelty detection: a review. II. Neural network based approaches
• Linear programming: foundations and extensions
• On the sparseness of 1-norm support vector machines
• Least angle regression. (With discussion)
• Support-vector networks
• Estimating the Support of a High-Dimensional Distribution
• 10.1162/15324430260185574
• 10.1162/15324430260185583
• Kernel Sparse Representation-Based Classifier

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