Support vector machine with customized kernel

Authors:
Guangyi Chen;Tien Dai Bui;Adam Krzyzak;Weihua Liu
Affiliations:
Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;State Key Lab. of Virtual Reality Technology and Systems, Beihang University, Beijing, P.R. China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

Citing 6
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Auto-correlation wavelet support vector machine

Image and Vision Computing
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Wavelet support vector machine

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The contourlet transform: an efficient directional multiresolution image representation

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the past two decades, Support Vector Machine (SVM) has become one of the most famous classification techniques. The optimal parameters in an SVM kernel are normally obtained by cross validation, which is a time-consuming process. In this paper, we propose to learn the parameters in an SVM kernel while solving the dual optimization problem. The new optimization problem can be solved iteratively as follows: (a) Fix the parameters in an SVM kernel; solve the variables αi in the dual optimization problem. (b) Fix the variables αi; solve the parameters in an SVM kernel by using the Newton-Raphson method. It can be shown that (a) can be optimized by using standard methods in training the SVM, while (b) can be solved iteratively by using the Newton-Raphson method. Experimental results conducted in this paper show that our proposed technique is feasible in practical pattern recognition applications.