DocumentCode :
2211505
Title :
A greedy algorithm for optimizing the kernel alignment and the performance of kernel machines
Author :
Pothin, Jean-Baptiste ; Richard, Cedric
Author_Institution :
Inst. des Sci. et Technol. de l´Inf. de Troyes, Univ. de Technol. de Troyes, Troyes, France
fYear :
2006
fDate :
4-8 Sept. 2006
Firstpage :
1
Lastpage :
4
Abstract :
Kernel-target alignment has recently been proposed as a criterion for measuring the degree of agreement between a reproducing kernel and a learning task. It makes possible to find a powerful kernel for a given classification problem without designing any classifier. In this paper, we present an alternating optimization strategy, based on a greedy algorithm for maximizing the alignment over linear combinations of kernels, and a gradient descent to adjust the free parameters of each kernel. Experimental results show an improvement in the classification performance of support vector machines, and a drastic reduction in the training time.
Keywords :
greedy algorithms; learning (artificial intelligence); optimisation; pattern classification; support vector machines; alternating optimization strategy; classification problem; greedy algorithm; kernel task; kernel-target alignment; learning task; support vector machines; Europe; Greedy algorithms; Kernel; Optimized production technology; Signal processing; Support vector machines; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference, 2006 14th European
Conference_Location :
Florence
ISSN :
2219-5491
Type :
conf
Filename :
7071040
Link To Document :
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