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- W1596105491 abstract "Ensemble learning improves the performance of a learning machine by using a majority vote of many weak-learners. As an alternative, Miyoshi and Okada proposed ensemble-teacher learning. In this method, the student learns from many quasi-optimal teachers and performs better than the quasi-optimal teachers when a linear perceptron is used. When a non-linear perceptron is used, a Hebbian rule is effective; however, a perceptron rule is not effective in this case and the student cannot perform better than the quasi-optimal teachers. In this paper, we analyze ensemble-teacher learning and explain why a perceptron rule is not effective in ensemble-teacher learning. We propose a method to overcome this problem." @default.
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- W1596105491 date "2010-01-01" @default.
- W1596105491 modified "2023-09-27" @default.
- W1596105491 title "On-Line Ensemble-Teacher Learning through a Perceptron Rule with a Margin" @default.
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- W1596105491 doi "https://doi.org/10.1007/978-3-642-15825-4_45" @default.
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