Ensemble-teacher learning through a perceptron rule with a margin
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
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The authors analyze the dynamics of online learning in a simple perceptron using a Gardner-style margin. The proposed method matches the perceptron rules for a margin of κ = 0 and the Hebb rules when κ → ∞. The results of analysis show that the generalization error is smaller than that of the perceptron rules and the Hebb rules during initial learning even though the proposed method is in between these two sets of learning rules. In addition, the authors show that the generalization error for the proposed method matches that of the perceptron rules in terms of asymptotic characteristics. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(7): 98–105, 2004; Published online in Wiley InterScience (). DOI 10.1002/scj.10473