TR Announcement

[Shirish K. Shevade]

Technical Report Announcement:

Smola and Sch\"{o}lkopf's SMO algorithm for SVM regression is
very simple and easy to implement. In a recent paper we
suggested some improvements to Platt's SMO algorithm for SVM 
classifier design. In this report we extend those ideas to Smola 
and Sch\"{o}lkopf's SMO algorithm for regression. The resulting
modified algorithms run much faster than the original SMO.
Details are given in the Technical Report mentioned below.
A gzipped postscript file containing the report can be downloaded 
from:
    http://guppy.mpe.nus.edu.sg/~mpessk/
Send any comments to: shirish at csa.iisc.ernet.in
 
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        Improvements to SMO Algorithm for SVM Regression

                 Technical Report CD-99-16

  S.K. Shevade, S.S. Keerthi, C. Bhattacharyya &  K.R.K. Murthy 


                         Abstract
      
This  paper  points out an important source of confusion and inefficiency
in Smola and Sch\"{o}lkopf's Sequential Minimal Optimization (SMO) algorithm 
for 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. These modified algorithms perform 
significantly faster than the original SMO on the datasets tried.

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