Abstract
This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and kernel principal component analysis (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 optical character recognition (OCR) and DNA analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 181-201 |
| Number of pages | 21 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 12 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2001 Mar |
Keywords
- Boosting
- Fisher's discriminant
- Kernel PCA
- Kernel methods
- Mathematical programming machines
- Mercer kernels
- Principal component analysis (PCA)
- Single-class classification
- Support vector machines (SVMs)
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence
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Cite this
Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001). An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2), 181-201. https://doi.org/10.1109/72.914517