Improving Generalization Ability through Active Learning.

Sethu Vijayakumar, Hidemitsu Ogawa

Research output: Contribution to journalArticlepeer-review


In this paper, we discuss the problem of active training data selection for improving the generalization capability of a neural network. We look at the learning problem from a function approximation perspective and formalize it as an inverse problem. Based on this framework, we analytically derive a method of choosing a training data set optimized with respect to the Wiener optimization criterion. The final result uses the apriori correlation information on the original function ensemble to devise an efficient sampling scheme which, when used in conjunction with the learning scheme described here, is shown to result in optimal generalization. This result is substantiated through a simulated example and a learning problem in high dimensional function space.
Original languageEnglish
Pages (from-to)480-487
JournalIEICE Transactions on Information and Systems
Issue number2
Publication statusPublished - 1999


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