Download PDF by Imre Csiszár (auth.), Yoav Freund, László Györfi, György: Algorithmic Learning Theory: 19th International Conference,

By Imre Csiszár (auth.), Yoav Freund, László Györfi, György Turán, Thomas Zeugmann (eds.)

ISBN-10: 3540879862

ISBN-13: 9783540879862

ISBN-10: 3540879870

ISBN-13: 9783540879879

This ebook constitutes the refereed complaints of the nineteenth foreign convention on Algorithmic studying thought, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the eleventh overseas convention on Discovery technological know-how, DS 2008.

The 31 revised complete papers offered including the abstracts of five invited talks have been conscientiously reviewed and chosen from forty six submissions. The papers are devoted to the theoretical foundations of desktop studying; they tackle subject matters reminiscent of statistical studying; likelihood and stochastic techniques; boosting and specialists; energetic and question studying; and inductive inference.

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Read Online or Download Algorithmic Learning Theory: 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008. Proceedings PDF

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Additional info for Algorithmic Learning Theory: 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008. Proceedings

Example text

The method builds adaptively a scoring function from training data with an ROC curve close to the optimal one. The approach relies on linear-by-parts approximations of the ROC curve which correspond to finitedimensional (piecewise constant) approximations of optimal scoring functions. As the ROC curve provides a performance measure of functional nature, the approximation can be conceived in a variety of ways depending on the topology equipping the space of ROC curves. For instance, the AUC is related to the L1 distance but we will also consider convergence to the optimal ROC curve in a stronger sense described by the L∞ -distance.

For d = 0, . . , D − 1 and for k = 0, . . ) Set the entropy measure: ˆ − (βd,k+1 − βd,k )α(C). ˆ Λd,k+1 (C) = (αd,k+1 − αd,k )β(C) Find the best subset Cd+1,2k of rectangle Cd,k in the AUC sense: Cd+1,2k = arg max Λd,k+1 (C) . C∈C, C⊂Cd,k Then, set Cd+1,2k+1 = Cd,k \ Cd+1,2k . ) Set ˆ d+1,2k ) ˆ (Cd+1,2k ) and βd+1,2k+1 = βd,k + β(C αd+1,2k+1 = αd,k + α as well as αd+1,2k+2 = αd,k+1 and βd+1,2k+2 = βd,k+1 . 3. Output. After D iterations, get the piecewise constant scoring function: 2D −1 (2D − k) I{x ∈ CD,k } sD (x) = k=0 The main features of the TreeRank algorithm are listed in the following remarks.

Sample Selection Bias Correction Theory Corinna Cortes1 , Mehryar Mohri1,2 , Michael Riley1 , and Afshin Rostamizadeh2 2 1 Google Research, 76 Ninth Avenue, New York, NY 10011 Courant Institute of Mathematical Sciences, 251 Mercer Street, New York, NY 10012 Abstract. This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution.

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Algorithmic Learning Theory: 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008. Proceedings by Imre Csiszár (auth.), Yoav Freund, László Györfi, György Turán, Thomas Zeugmann (eds.)


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