March 1, 2001

Improvements to Platt's SMO Algorithm for SVM Classifier Design

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Abstract

This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm 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. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.

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Authors

S. Sathiya Keerthi

Shirish Shevade

Chiranjib Bhattacharyya

Journals

Neural Computation

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Institutions

National University of Singapore

Indian Institute of Science Bangalore

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Keerthi et al. (Thu,) studied this question.

www.synapsesocial.com/papers/6a09b80616dfdfe7ed345104 — DOI: https://doi.org/10.1162/089976601300014493

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Improvements to Platt's SMO Algorithm for SVM Classifier Design

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Abstract

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References and Citations

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Cite this study

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