Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

December 1, 2000, Vol. 12, No. 12, Pages 2909-2940
(doi: 10.1162/089976600300014773)
© 2000 Massachusetts Institute of Technology
Incremental Active Learning for Optimal Generalization
Article PDF (360.18 KB)
Abstract

The problem of designing input signals for optimal generalization is called active learning. In this article, we give a two-stage sampling scheme for reducing both the bias and variance, and based on this scheme, we propose two active learning methods. One is the multipoint search method applicable to arbitrary models. The effectiveness of this method is shown through computer simulations. The other is the optimal sampling method in trigonometric polynomial models. This method precisely specifies the optimal sampling locations.