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Data Condensation in Large Databases by Incremental Learning with Support Vector Machines

9 years 7 months ago
Data Condensation in Large Databases by Incremental Learning with Support Vector Machines
An algorithmfor data condensation using support vector machines (SVM's)is presented. The algorithm extracts datapoints lying close to the class boundaries,whichform a much reducedbut critical setfor classification. Theproblem of large memory requirementsfor training SVM's in batch mode is circumvented by adopting an active incremental learning algorithm. The learning strategy is motivatedfrom the condensed nearest neighbor classification technique. Experimental resultspresented show that such active incremental learning enjoy superiority in terms of computation time and condensation ratio, over related methods.
Pabitra Mitra, C. A. Murthy, Sankar K. Pal
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2000
Where ICPR
Authors Pabitra Mitra, C. A. Murthy, Sankar K. Pal
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