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Incremental Active Learning with Bias Reduction
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.857807IEEE-INNS-ENNS International Joint Co ...
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Masashi Sugiyama, Tokyo Institute of Technology
Hidemitsu Ogawa, Tokyo Institute of Technology
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 zero. In this paper, we remove this assumption and propose a new active learning method with the bias reduction. The effectiveness of the proposed methods demonstrated through computer simulations.
Citation:
Masashi Sugiyama, Hidemitsu Ogawa, "Incremental Active Learning with Bias Reduction," ijcnn, vol. 1, pp.1015, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 1, 2000
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