What's New?
Gunnar Rätsch, Dr.
Publications
Using Support Vector Machines for Time Series Prediction
Document Actions
S Mika, G Rätsch, J Weston, B Schölkopf, and K Müller (1999)
Fisher Discriminant Analysis with Kernels
In: Proc. NNSP'99, ed. by Y. - H. Hu, J. Larsen, E. Wilson and S. Douglas, pp. 41-48, IEEE.
A non–linear classification technique based on Fisher's discriminant is proposed. Main ingredient is the kernel trick which allows to efficiently compute the linear Fisher discriminant in feature space. The linear classification in feature space corresponds to a powerful non–linear decision function in input space. Large scale simulations demonstrate the competitiveness of our approach.
The group will join the Sloan-Kettering Institute in New York City in January 2012
