Title :
Improved neural network for SVM learning
Author :
Anguita, Davide ; Boni, Andrea
Author_Institution :
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
fDate :
9/1/2002 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2002.1031958
