Title :
Analog neural network for support vector machine learning
Author :
Perfetti, Renzo ; Ricci, Elisa
Author_Institution :
Dept. of Electron. & Inf. Eng., Perugia Univ.
fDate :
7/1/2006 12:00:00 AM
Abstract :
An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems
Keywords :
learning (artificial intelligence); neural chips; quadratic programming; support vector machines; analog neural network; partially dual formulation; quadratic programming problem; support vector machine learning; Application software; Circuits; Computer simulation; Kernel; Machine learning; Neural networks; Quadratic programming; Recurrent neural networks; Support vector machine classification; Support vector machines; Analog circuits; quadratic optimization; recurrent neural networks; support vector machines; Computers, Analog; Learning; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.875967
