Download e-book for iPad: Algorithmic Learning Theory: 27th International Conference, by Ronald Ortner, Hans Ulrich Simon, Sandra Zilles

International 1

By Ronald Ortner, Hans Ulrich Simon, Sandra Zilles

ISBN-10: 3319463780

ISBN-13: 9783319463780

ISBN-10: 3319463799

ISBN-13: 9783319463797

This publication constitutes the refereed complaints of the twenty seventh overseas convention on Algorithmic studying conception, ALT 2016, held in Bari, Italy, in October 2016, co-located with the nineteenth overseas convention on Discovery technological know-how, DS 2016. The 24 typical papers awarded during this quantity have been rigorously reviewed and chosen from forty five submissions. additionally the e-book includes five abstracts of invited talks. The papers are equipped in topical sections named: blunders bounds, pattern compression schemes; statistical studying, concept, evolvability; special and interactive studying; complexity of training types; inductive inference; on-line studying; bandits and reinforcement studying; and clustering.

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Additional info for Algorithmic Learning Theory: 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings

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Our generalized sample compression scheme for extremal classes is still easy to describe. However its analysis requires more combinatorics and heavily exploits the rich structure of extremal classes. Despite being more general, the construction is simple. We also give explicit examples of maximal classes that are extremal but not maximum (see Example 5). We also discuss a certain greedy peeling method for producing an unlabeled compressions scheme. Such schemes were first conjectured in [17] and later proven to exist for maximum classes [28].

03101 9. : On the algorithmic implementation of multiclass kernelbased vector machines. J. Mach. Learn. Res. 2, 265–292 (2002) 10. : Regularization techniques for learning with matrices. J. Mach. Learn. Res. 13, 1865–1890 (2012) 11. : Empirical margin distributions and bounding the generalization error of combined classifiers. Ann. Stat. 30(1), 1–50 (2002) 12. : Probability in Banach Spaces: Isoperimetry and Processes. Springer, Berlin (1991) 13. : Multi-class SVMs: from tighter datadependent generalization bounds to novel algorithms.

Com Abstract. In statistical learning the excess risk of empirical risk minα n (F ) , where n is a size of imization (ERM) is controlled by COMP n a learning sample, COMPn (F ) is a complexity term associated with a given class F and α ∈ [ 12 , 1] interpolates between slow and fast learning rates. In this paper we introduce an alternative localization approach for binary classification that leads to a novel complexity measure: fixed points of the local empirical entropy. We show that this complexity measure gives a tight control over COMPn (F ) in the upper bounds under bounded noise.

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Algorithmic Learning Theory: 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings by Ronald Ortner, Hans Ulrich Simon, Sandra Zilles


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