DocumentCode :
1735282
Title :
Deep Multiple Kernel Learning
Author :
Strobl, Eric V. ; Visweswaran, Shyam
Author_Institution :
Dept. of Biomed. Inf., Univ. of Pittsburgh, Pittsburgh, PA, USA
Volume :
1
fYear :
2013
Firstpage :
414
Lastpage :
417
Abstract :
Deep learning methods have predominantly been applied to large artificial neural networks. Despite their state-of-the-art performance, these large networks typically do not generalize well to datasets with limited sample sizes. In this paper, we take a different approach by learning multiple layers of kernels. We combine kernels at each layer and then optimize over an estimate of the support vector machine leave-one-out error rather than the dual objective function. Our experiments on a variety of datasets show that each layer successively increases performance with only a few base kernels.
Keywords :
learning (artificial intelligence); neural nets; support vector machines; artificial neural network; deep multiple kernel learning; leave-one-out error; support vector machine; Accuracy; Complexity theory; Kernel; Linear programming; Support vector machines; Training; Upper bound; Deep Learning; Kernels; Multiple Kernel Learning; Support Vector Machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications (ICMLA), 2013 12th International Conference on
Conference_Location :
Miami, FL
Type :
conf
DOI :
10.1109/ICMLA.2013.84
Filename :
6784654
Link To Document :
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