A tutorial on ν‐support vector machines

DOI10.1002/asmb.537zbMath1097.93040OpenAlexW1932600547WikidataQ60241070 ScholiaQ60241070MaRDI QIDQ5467277

Pai-Hsuen Chen, Bernhard Schölkopf, Chih-Jen Lin

Publication date: 24 May 2006

Published in: Applied Stochastic Models in Business and Industry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/asmb.537

zbMATH Keywords

positive definite kernelsstatistical learning theorysupport vector regression\(\nu\)-support vector machinessupport vector implementation

Mathematics Subject Classification ID

Stochastic learning and adaptive control (93E35)

Related Items (12)

Robust ν-support vector machine based on worst-case conditional value-at-risk minimization ⋮ An approximate linear solver in least square support vector machine using randomized singular value decomposition ⋮ Incremental learning for \(\nu\)-support vector regression ⋮ Budget constrained non-monotonic feature selection ⋮ Linear convergence rate for the MDM algorithm for the nearest point problem ⋮ Accurate on-line \(\nu\)-support vector learning ⋮ Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition ⋮ Support vector machines based on convex risk functions and general norms ⋮ Nearest neighbors distance ratio open-set classifier ⋮ A set of new Chebyshev kernel functions for support vector machine pattern classification ⋮ A support vector machine-based ensemble algorithm for breast cancer diagnosis ⋮ Geometric insights into support vector machine behavior using the KKT conditions

Uses Software

• SVMlight

Cites Work

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• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Support vector machines are universally consistent
• Support-vector networks
• Formulations of Support Vector Machines: A Note from an Optimization Point of View
• Training v-Support Vector Classifiers: Theory and Algorithms
• A duality theorem for non-linear programming
• Scale-sensitive dimensions, uniform convergence, and learnability
• Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators

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