An Improved Training Algorithm for Kernel Fisher Discriminants

Sebastian Mika; Alexander J. Smola; Bernhard Sch�lkopf

Venue: Conference on Artificial Intelligence and Statistics (AISTATS) 2001
Recognition: Most Influential AISTATS 2001 Paper (Rank No. 5)
Edition: 2026-03
Impact factor: 3
Certificate ID: b9b4f9ff84d1ca5a

Abstract

We present a fast training algorithm for the kernel Fisher discriminant classifier. It uses a greedy approximation technique and has an empirical scaling behavior which improves upon the state of the art by more than an order of magnitude, thus rendering the kernel Fisher algorithm a viable option also for large datasets.

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