AN INTRODUCTION TO KERNEL - BASED LEARNING ALGORITHMS 183 Empirical Risk

نویسندگان

  • Sebastian Mika
  • Gunnar Ratsch
  • Koji Tsuda
  • Bernhard Scholkopf
چکیده

| This review provides an introduction to Support Vector Machines, Kernel Fisher Discriminant analysis and Kernel PCA, as examples for successful kernel based learning methods. We rst give a short background about 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 nally discussing applications such as OCR and DNA analysis. Keywords| Kernel methods, Support Vector Machines, Fisher's discriminant, Mathematical Programming Machines, PCA, Kernel PCA, single-class classi cation, Boosting, Mercer Kernels.

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تاریخ انتشار 2003