Learning from Minimum Entropy Queries in a Large Committee Machine

Details Learning from Minimum Entropy Queries in a Large Committee Machine Learning from Minimum Entropy Queries in a Large Committee Machine

1996-04-11

arxiv.org/abs/adap-org/9604001v1

Physical Review E Vol 53 (1996) pp. R2060-R2063

Physics

Condensed Matter

4 pages, REVTeX, multicol, epsf, two postscript figures. To appear in Physical Review E (Rapid Communications)

Scientific paper

10.1103/PhysRevE.53.R2060

In supervised learning, the redundancy contained in random examples can be avoided by learning from queries. Using statistical mechanics, we study learning from minimum entropy queries in a large tree-committee machine. The generalization error decreases exponentially with the number of training examples, providing a significant improvement over the algebraic decay for random examples. The connection between entropy and generalization error in multi-layer networks is discussed, and a computationally cheap algorithm for constructing queries is suggested and analysed.

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