DocumentCode :
9865
Title :
MKBoost: A Framework of Multiple Kernel Boosting
Author :
Xia, Hao ; Hoi, Steven C.H.
Author_Institution :
Nanyang Technological University, Singapore
Volume :
25
Issue :
7
fYear :
2013
fDate :
Jul-13
Firstpage :
1574
Lastpage :
1586
Abstract :
Multiple kernel learning (MKL) is a promising family of machine learning algorithms using multiple kernel functions for various challenging data mining tasks. Conventional MKL methods often formulate the problem as an optimization task of learning the optimal combinations of both kernels and classifiers, which usually results in some forms of challenging optimization tasks that are often difficult to be solved. Different from the existing MKL methods, in this paper, we investigate a boosting framework of MKL for classification tasks, i.e., we adopt boosting to solve a variant of MKL problem, which avoids solving the complicated optimization tasks. Specifically, we present a novel framework of Multiple kernel boosting (MKBoost), which applies the idea of boosting techniques to learn kernel-based classifiers with multiple kernels for classification problems. Based on the proposed framework, we propose several variants of MKBoost algorithms and extensively examine their empirical performance on a number of benchmark data sets in comparisons to various state-of-the-art MKL algorithms on classification tasks. Experimental results show that the proposed method is more effective and efficient than the existing MKL techniques.
Keywords :
Boosting; Complexity theory; Kernel; Optimization; Support vector machines; Training; Training data; Multiple kernel learning; boosting; classification; kernel methods;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2012.89
Filename :
6189347
Link To Document :
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