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- W2034451938 abstract "We introduce an inferential approach to unsupervised learning which allows us to define an optimal learning strategy. Applying these ideas to a simple, previously studied model, we show that it is impossible to detect structure in data until a critical number of examples have been presented-an effect which will be observed in all problems with certain underlying symmetries. Thereafter, the advantage of optimal learning over previously studied learning algorithms depends critically upon the distribution of patterns; optimal learning may be exponentially faster. Models with more subtle correlations are harder to analyse, but in a simple limit of one such problem we calculate exactly the efficacy of an algorithm similar to some used in practice, and compare it to that of the optimal prescription." @default.
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- W2034451938 date "1994-03-21" @default.
- W2034451938 modified "2023-09-25" @default.
- W2034451938 title "Optimal unsupervised learning" @default.
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- W2034451938 doi "https://doi.org/10.1088/0305-4470/27/6/016" @default.
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