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Recursive SVM based on TEDA

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Published
Publication date2015
Host publicationStatistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings
EditorsAlexander Gammerman, Vladimir Vovk, Harris Papadopoulos
Place of PublicationCham
PublisherSpringer
Pages156-168
Number of pages13
ISBN (Electronic)9783319170916
ISBN (Print)9783319170909
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9047
ISSN (Print)0302-9743

Abstract

The new method for incremental learning of SVM model incorporating recently proposed TEDA approach is proposed. The method updates the widely renowned incremental SVM approach, as well as introduces new TEDA and RDE kernels which are learnable and capable of adaptation to data. The slack variables are also adaptive and depend on each point’s ‘importance’ combining the outliers detection with SVM slack variables to deal with misclassifications. Some suggestions on the evolving systems based on SVM are also provided. The examples of image recognition are provided to give a ‘proof of concept’ for the method.