DocumentCode :
1739149
Title :
DirectSVM: a fast and simple support vector machine perceptron
Author :
Roobaert, Danny
Author_Institution :
Comput. Vision & Active Perception Lab., R. Inst. of Technol., Stockholm, Sweden
Volume :
1
fYear :
2000
fDate :
2000
Firstpage :
356
Abstract :
We propose a simple implementation of the support vector machine (SVM) for pattern recognition, that is not based on solving a complex quadratic optimization problem. Instead we propose a simple, iterative algorithm that is based on a few simple heuristics. The proposed algorithm finds high-quality solutions in a fast and intuitively-simple way. In experiments on the COIL database, on the extended COIL database and on the Sonar database of the UCI Irvine repository, DirectSVM is able to find solutions that are similar to these found by the original SVM. However DirectSVM is able to find these solutions substantially faster, while requiring less computational resources than the original SVM
Keywords :
learning automata; neural nets; pattern recognition; perceptrons; COIL database; DirectSVM; Sonar database; Statistical Learning Theory; high-quality solutions; iterative algorithm; learning algorithms; pattern recognition; support vector machine perceptron; Automatic control; Control systems; Databases; Iterative algorithms; Neural networks; Neurons; Pattern recognition; Quadratic programming; Statistical learning; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop
Conference_Location :
Sydney, NSW
ISSN :
1089-3555
Print_ISBN :
0-7803-6278-0
Type :
conf
DOI :
10.1109/NNSP.2000.889427
Filename :
889427
Link To Document :
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