This chapter provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel PCA as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis (VC) theory and kernel feature spaces and then proceed to kernel-based learning in supervised and unsupervised scenarios, including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by finally discussing applications such as OCR and DNA analysis.
|Title of host publication||Handbook of Neural Network Signal Processing|
|Publication status||Published - 2001 Jan 1|
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)