Learning of Non-Monotonic Target Functions by Ising Perceptrons : Learning Curve, Perfect Learning and Perfect Anti-Learning (General)
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概要
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We study supervised learning by Ising perceptrons for a class of non-monotonic teacher input-output relations employing the replica method with the Gibbs algorithm. We consider up to one-step replica symmetry breaking (1RSB) solutions. We find that the behaviour of the learning curve depends on the shape of the teacher input-output relation. In particular, for the class of teacher input-output relations, which represents an extension of the reversed-wedge problem, a peculiar type of learning, perfect anti-learning (PAL), exists, whereas perfect learning (PL) does not exist. Further, we find that the students' vectors cannot tend to the teacher vector, that is, the students cannot learn the teacher completely. The theoretical results are confirmed by the results of Monte Carlo simulations.
- 社団法人日本物理学会の論文
- 2003-07-15
著者
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Uezu Tatsuya
Graduate School Of Humanities And Sciences Nara Women's University
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Uezu Tatsuya
Graduate School Of Human Culture Nara Women's University
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TOORIYAMA Kanako
Graduate School of Humanities and Sciences, Nara Women's University
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Tooriyama Kanako
Graduate School Of Humanities And Sciences Nara Women's University
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