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Abstract: The problem of designing input signals for optimal generalization in supervised learning is called active learning. In many active learning methods devised so far, the bias of the learning results is assumed to be zero. In this paper, we remove this assumption and propose a new active learning method with the bias reduction. The e#ectiveness of the proposed method is demonstrated through computer simulations. 1 Introduction Supervised learning is obtaining an underlying rule from sampled... (Update)
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BibTeX entry: (Update)
@misc{ sugiyama-incremental,
author = "Masashi Sugiyama and Hidemitsu Ogawa",
title = "Incremental Active Learning with Bias Reduction",
url = "citeseer.ist.psu.edu/sugiyama99incremental.html" }
Citations (may not include all citations):132 Theory of optimal experiments (context) - Fedorov - 1972
105 Information-based objective functions for active data select.. - MacKay - 1992
77 Neural network exploration using optimal experiment design - Cohn - 1994
24 Active learning in multilayer perceptrons - Fukumizu - 1996
16 Projection filter regularization of ill-conditioned problem (context) - Ogawa - 1987
11 Neural network learning (context) - Ogawa - 1992
5 Training data selection for optimal generalization in trigon.. - Sugiyama, Ogawa
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