Abstract
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4.1. General framework
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4.2. Dichotomic histograms
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4.3. Mathematical framework for density estimation
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4.4. Main oracle inequality
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4.5. Checking the accuracy of the bounds on the Gaussian shift model
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4.6. Application to adaptive classification
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4.6.1. Randomized classification rule, general case
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4.6.2. Randomized classification rule for “non-ambiguous” classification problems
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4.6.3. Deterministic classification rule
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4.6.4. Counter example
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4.7. Two stage adaptive least square regression
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4.8. One stage piecewise constant regression
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4.9. Some abstract inference problem
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4.10. Another type of bound
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© 2004 Springer-Verlag Berlin/Heidelberg
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Catoni, O. (2004). 4. Gibbs estimators. In: Picard, J. (eds) Statistical Learning Theory and Stochastic Optimization. Lecture Notes in Mathematics, vol 1851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44507-4_5
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DOI: https://doi.org/10.1007/978-3-540-44507-4_5
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