articleTop 10% cited
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks · 2000 · Vol. 11(5) · pp. 1188–1193
Shirish Shevade✉(Indian Institute of Science Bangalore)S. Sathiya Keerthi(National University of Singapore)Chiranjib Bhattacharyya(Indian Institute of Science Bangalore)K. R. K. Murthy(Indian Institute of Science Bangalore)
Abstract
This paper points out an important source of inefficiency in Smola and Schölkopf's sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried.
Face and Expression RecognitionMetaheuristic Optimization Algorithms ResearchNeural Networks and ApplicationsSequential minimal optimizationSupport vector machineComputer scienceAlgorithmRegressionInefficiencyRegression analysisDual (grammatical number)Artificial intelligenceMachine learning
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UCI Repository of machine learning databases
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