Kernel method in pattern recognition and classification

Junbin Gao

doi:10.1117/12.441450

21 September 2001 Kernel method in pattern recognition and classification

Junbin Gao

Proceedings Volume 4550, Image Extraction, Segmentation, and Recognition; (2001) https://doi.org/10.1117/12.441450
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

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.

Citation Download Citation

Junbin Gao "Kernel method in pattern recognition and classification", Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); https://doi.org/10.1117/12.441450

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