| Issue |
J. Phys. I France
Volume 2, Number 2, February 1992
|
|
|---|---|---|
| Page(s) | 167 - 180 | |
| DOI | https://doi.org/10.1051/jp1:1992131 | |
DOI: 10.1051/jp1:1992131
J. Phys. I France 2 (1992) 167-180
1 Theoretical Physics, Oxford University, 1 Keble Road, GB-Oxford OX1 3NP, G.B.
2 Inst. voor Theor. Fysica, K. U. Leuven, B-3001 Leuven, Belgium
© Les Editions de Physique 1992
J. Phys. I France 2 (1992) 167-180
Learning multi-class classification problems
Timothy L. H. Watkin1, Albrecht Rau1, Desiré Bollé1 and Jort van Mourik21 Theoretical Physics, Oxford University, 1 Keble Road, GB-Oxford OX1 3NP, G.B.
2 Inst. voor Theor. Fysica, K. U. Leuven, B-3001 Leuven, Belgium
(Received 26 August 1991, accepted in final form 20 October 1991)
Abstract
A multi-class perceptron can learn from examples to solve problems whose answer may take several different values. Starting
from a general formalism, we consider the learning of rules by a Hebbian algorithm and by a Monte-Carlo algorithm at high
temperature. In the benchmark "prototype-problem" we show that a simple rule may be more than an order of magnitude more efficient
than the well-known solution, and in the conventional limit is in fact optimal. A multi-class perceptron is significantly
more efficient than a more complicated architecture of binary perceptrons.
© Les Editions de Physique 1992