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

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

This e-book constitutes the refereed court cases of the nineteenth foreign convention on Algorithmic studying concept, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the eleventh foreign convention on Discovery technological know-how, DS 2008.

The 31 revised complete papers provided 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 laptop studying; they tackle themes resembling statistical studying; likelihood and stochastic tactics; boosting and specialists; energetic and question studying; and inductive inference.

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

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3559, pp. 1–15. : Ranking and empirical risk minimization of U-statistics. : Auc optimization vs. error rate minimization. , Sch¨ olkopf, B. ) Advances in Neural Information Processing Systems 16. : Tree-structured ranking rules and approximation of the optimal ROC curve. : A Probabilistic Theory of Pattern Recognition. : Constructive Approximation. : Signal Detection Theory and ROC Analysis. : Learning decision trees using the area under the roc curve. In: ICML 2002: Proceedings of the Nineteenth International Conference on Machine Learning, pp.

Remarkably, it is often possible to correct this bias by using large amounts of unlabeled data. The problem of sample selection bias correction for linear regression has been extensively studied in econometrics and statistics (Heckman, 1979; Little & Rubin, 1986) with the pioneering work of Heckman (1979). , 2006) have also dealt with this problem. The main correction technique used in all of these publications consists of reweighting the cost of training point errors to more closely reflect that of the test distribution.

With some additional technicalities, this argument can be iterated. The recurrence leads to the next lemma which is the key for the proof of the theorem. Lemma 1. Under the assumptions of Theorem 2, there exist constants κ1 , κ2 , c1 and c2 such that, for all δ > 0, we have with probability at least 1 − δ: ∀d ∈ N, ∀n ∈ N, B(d, n, δ), |AUC(s∗d ) − AUC(sd )| ≤ κd−1 1 and ∀k ∈ {0, . . , 2d−1 − 1}, ∗ ∗ ) − α(Cd,2k )| + |β(Cd,2k ) − β(Cd,2k )| ≤ κd2 B(d + 1, n, δ), |α(Cd,2k where: ∀(d, n, δ) ∈ N × N×]0, 1[, B(d, n, δ) = c21 V n 1 2d + c22 log(1/δ) n 1 2d .

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