Multi-scale Support Vector Machine Optimization by Kernel Target-Alignment

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Áreas de investigación:
Año:
2013
Tipo de publicación:
Artículo en conferencia
Autores:
Título del libro:
21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2013)
Páginas:
391-396
Dirección:
Brudge
Organización:
Bruges, Belgium
Mes:
24th-25th April
ISBN:
978-2-87419-081-0
BibTex:
Abstract:
The problem considered is the optimization of a multi-scale kernel, where a different width is chosen for each feature. This idea has been barely studied in the literature, and through the use of evolutionary or gradient descent approaches, which explicitly train the learning machine and thereby incur high computacional cost. To cope with this limitation, the problem is explored by making use of an analytical methodology known as kernel-target alignment, where the kernel is optimized by aligning it to the so-called ideal kernel matrix. The results show that the proposal leads to better performance and simpler models at limited computational cost when applying the binary Support Vector Machine (SVM) paradigm.
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