DocumentCode
814582
Title
Improved neural network for SVM learning
Author
Anguita, Davide ; Boni, Andrea
Author_Institution
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
Volume
13
Issue
5
fYear
2002
fDate
9/1/2002 12:00:00 AM
Firstpage
1243
Lastpage
1244
Abstract
The recurrent network of Xia et al. (1996) was proposed for solving quadratic programming problems and was recently adapted to support vector machine (SVM) learning by Tan et al. (2000). We show that this formulation contains some unnecessary circuits which, furthermore, can fail to provide the correct value of one of the SVM parameters and suggest how to avoid these drawbacks.
Keywords
learning (artificial intelligence); learning automata; quadratic programming; recurrent neural nets; SVM learning; differential equation; optimization; quadratic programming problem; recurrent neural network; support vector machine; Circuits; Differential equations; Hardware; Machine learning; Neural networks; Proposals; Quadratic programming; Support vector machine classification; Support vector machines; Very large scale integration;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/TNN.2002.1031958
Filename
1031958
Link To Document