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- W1497148917 abstract "The methods of information theory provide natural approaches to learning algorithms in the case of stochastic formal neural networks. Most of the classical techniques are based on some extremization principle. A geometrical interpretation of the associated algorithms provides a powerful tool for understanding the learning process and its stability and offers a framework for discussing possible new learning rules. An illustration is given using sequential and parallel learning in the Boltzmann machine." @default.
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- W1497148917 date "2001-01-01" @default.
- W1497148917 modified "2023-09-27" @default.
- W1497148917 title "Geometrical methods in learning theory" @default.
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- W1497148917 doi "https://doi.org/10.1063/1.1358171" @default.
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