On generalization performance and non-convex optimization of extended \(\nu \)-support vector machine

DOI10.1007/s00354-008-0064-6zbMath1183.68508OpenAlexW2042641209MaRDI QIDQ1031941

Masashi Sugiyama, Akiko Takeda

Publication date: 23 October 2009

Published in: New Generation Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00354-008-0064-6

zbMATH Keywords

global optimizationsupport vector machineconditional value-at-riskE\(\nu \)-SVCgeneralization error bound

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Optimizing over coherent risk measures and non-convexities: a robust mixed integer optimization approach

Uses Software

• UCI-ml
• AdaBoost.MH

Cites Work

• Quasi-concave minimization subject to linear constraints
• A decision-theoretic generalization of on-line learning and an application to boosting
• Boosting the margin: a new explanation for the effectiveness of voting methods
• Support-vector networks
• Training v-Support Vector Classifiers: Theory and Algorithms
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