Kernel method in pattern recognition and classification
Junbin Gao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
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.