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Proceedings Paper

Kernel method in pattern recognition and classification
Author(s): Junbin Gao
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Paper Abstract

Kernel based methods and Support Vector Machines (SVMs)\cite{Vapnik1998,Smola1998} in particular are a class of learning methods that can be used for non-linear regression estimation. They have often achieved state of the art performance in many areas where they have been applied. The class of functions they choose from is determined by a kernel function. The form of this function is of central importance to kernel based methods. In this topic, I will give a simple description about the core concept of kernel-based methods and SVM and some fresh ideas for creating new kernels with multiscale and interpretability characterizations.

Paper Details

Date Published: 21 September 2001
PDF: 10 pages
Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); doi: 10.1117/12.441450
Show Author Affiliations
Junbin Gao, Univ. of Southampton (Australia)


Published in SPIE Proceedings Vol. 4550:
Image Extraction, Segmentation, and Recognition

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