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Rigorous Learning Curve Bounds from Statistical Mechanics (1994)  (Make Corrections)  (47 citations)
David Haussler, Michael Kearns, H. Sebastian Seung, Naftali Tishby
Computational Learing Theory



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Abstract: this paper, we show that ideas from statistical mechanics (namely, the annealed approximation [20, 1, 21] and the thermodynamic limit [21]) can be used as the basis of a mathematically precise and rigorous theory of learning curves. (Update)

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BibTeX entry:   (Update)

Haussler, D., Kearns, M., Seung, H., and Tishby, N. (1996). Rigorous learning curve bounds from statistical mechanics. MACHINE LEARNING, 25:195--236. http://citeseer.ist.psu.edu/article/haussler94rigorous.html   More

@inproceedings{ haussler94rigorous,
    author = "David Haussler and H. Sebastian Seung and Michael J. Kearns and Naftali Tishby",
    title = "Rigorous Learning Curve Bounds from Statistical Mechanics",
    booktitle = "Computational Learing Theory",
    pages = "76-87",
    year = "1994",
    url = "citeseer.ist.psu.edu/article/haussler94rigorous.html" }
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348   Estimation of Dependences Based on Empirical Data (context) - Vapnik - 1982
318   Convergence of Stochastic Processes (context) - Pollard - 1984
268   Decision-theoretic generalizations of the PAC model for neur.. (context) - Haussler - 1992
85   Bounds on the sample complexity of Bayesian learning using i.. - Haussler, Kearns et al. - 1991
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58   Statistical mechanics of learning from examples (context) - Seung, Sompolinsky et al. - 1992
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37   The space of interactions in neural network models (context) - Gardner - 1988
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