Feasible direction decomposition algorithms for training support vector machines

DOI10.1023/A:1012479116909zbMath1050.68125OpenAlexW1512068198MaRDI QIDQ5959965

Pavel Laskov

Publication date: 11 April 2002

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1012479116909

zbMATH Keywords

working set selection

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

A Note on the Decomposition Methods for Support Vector Regression, On the complexity of working set selection, A faster gradient ascent learning algorithm for nonlinear SVM, Parameter selection of support vector regression based on hybrid optimization algorithm and its application, Neighborhood Property–Based Pattern Selection for Support Vector Machines, An efficient support vector machine learning method with second-order cone programming for large-scale problems, Training v-Support Vector Regression: Theory and Algorithms

Uses Software

• SVMlight