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NIPS
2000
13 years 9 months ago
A Silicon Primitive for Competitive Learning
Competitive learning is a technique for training classification and clustering networks. We have designed and fabricated an 11transistor primitive, that we term an automaximizing ...
David Hsu, Miguel Figueroa, Chris Diorio
NIPS
2000
13 years 9 months ago
Feature Selection for SVMs
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This sear...
Jason Weston, Sayan Mukherjee, Olivier Chapelle, M...
NIPS
2000
13 years 9 months ago
Incremental and Decremental Support Vector Machine Learning
An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the KuhnTucker conditions on all previously se...
Gert Cauwenberghs, Tomaso Poggio
NIPS
2000
13 years 9 months ago
Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra
A system has been developed to extract diagnostic information from jet engine carcass vibration data. Support Vector Machines applied to novelty detection provide a measure of how...
Paul Hayton, Bernhard Schölkopf, Lionel Taras...
NIPS
2000
13 years 9 months ago
Foundations for a Circuit Complexity Theory of Sensory Processing
We introduce total wire length as salient complexity measure for an analysis of the circuit complexity of sensory processing in biological neural systems and neuromorphic engineer...
Robert A. Legenstein, Wolfgang Maass