Download Algorithmic Learning Theory: 15th International Conference, by Ayumi Shinohara (auth.), Shoham Ben-David, John Case, Akira PDF

By Ayumi Shinohara (auth.), Shoham Ben-David, John Case, Akira Maruoka (eds.)

Algorithmic studying thought is arithmetic approximately desktop courses which study from event. This consists of huge interplay among quite a few mathematical disciplines together with thought of computation, information, and c- binatorics. there's additionally huge interplay with the sensible, empirical ?elds of desktop and statistical studying within which a vital target is to foretell, from earlier info approximately phenomena, necessary positive factors of destiny facts from an identical phenomena. The papers during this quantity disguise a large diversity of themes of present study within the ?eld of algorithmic studying conception. we've divided the 29 technical, contributed papers during this quantity into 8 different types (corresponding to 8 classes) re?ecting this vast variety. the types featured are Inductive Inf- ence, Approximate Optimization Algorithms, on-line series Prediction, S- tistical research of Unlabeled facts, PAC studying & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. under we provide a quick review of the ?eld, putting each one of those themes within the normal context of the ?eld. Formal versions of automatic studying re?ect numerous aspects of the wide variety of actions that may be seen as studying. A ?rst dichotomy is among viewing studying as an inde?nite approach and viewing it as a ?nite job with a de?ned termination. Inductive Inference types concentrate on inde?nite studying procedures, requiring merely eventual good fortune of the learner to converge to a passable conclusion.

Show description

Read Online or Download Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings PDF

Similar education books

Advances in Learning Classifier Systems: Third International Workshop, IWLCS 2000 Paris, France, September 15–16, 2000 Revised Papers

Studying classi er structures are rule-based platforms that make the most evolutionary c- putation and reinforcement studying to unravel di cult difficulties. They have been - troduced in 1978 by means of John H. Holland, the daddy of genetic algorithms, and because then they've been utilized to domain names as diversified as self sufficient robotics, buying and selling brokers, and information mining.

Goffman and Social Organisation: Studies in a Sociological Legacy

Erving Goffman is taken into account through many to were some of the most very important sociologists of the post-war period. His shut commentary of lifestyle and his crisis with the ways that humans play roles and deal with the impressions they current to one another ended in his pioneering construction of a brand new dramaturgical point of view for sociology.

Is Taiwan Chinese?: The Impact of Culture, Power, and Migration on Changing Identities (Interdisciplinary Studies of China, 2)

The "one China" coverage formally supported by way of the People's Republic of China, the U.S., and different international locations asserts that there's just one China and Taiwan is part of it. the controversy over even if the folks of Taiwan are chinese language or independently Taiwanese is, Melissa J. Brown argues, an issue of id: Han ethnic id, chinese language nationwide identification, and the connection of either one of those to the recent Taiwanese id cast within the Nineteen Nineties.

Additional info for Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings

Example text

Even though that is generally true, there exist specific situations for which this is feasible. Indeed, consider tree banks such as the UPenn Wall Street Journal corpus [27], which contain parse trees. These trees directly correspond to the proof-trees we talk about. Even 24 L. De Raedt and K. Kersting though – to the best of the authors’ knowledge (but see [43,3] for inductive logic programming systems that learn from traces) – no inductive logic programming system has been developed to learn from proof-trees, it is not hard to imagine an outline for such an algorithm.

41] J. Quinlan and R. Cameron-Jones. Induction of logic programs: FOIL and related systems. New Generation Computing, 13(3–4):287–312, 1995. [42] T. Sato. A Statistical Learning Method for Logic Programs with Distribution Semantics. In L. Sterling, editor, Proceedings of the Twelfth International Conference on Logic Programming (ICLP-1995), pages 715 – 729, Tokyo, Japan, 1995. MIT Press. 36 L. De Raedt and K. Kersting [43] E. Shapiro. Algorithmic Program Debugging. MIT Press, 1983. [44] A. Srinivasan.

Marcus, M. Marcinkiewicz, and B. Santorini. Building a large annotated corpus of English: The Penn TREEBANK. Computational Linguistics, 19(2):313–330, 1993. [28] G. McKachlan and T. Krishnan. The EM Algorithm and Extensions. , 1997. [29] T. M. Mitchell. Machine Learning. , 1997. [30] S. Muggleton. Inverse Entailment and Progol. New Generation Computing Journal, 13(3–4):245–286, 1995. [31] S. Muggleton. Stochastic logic programs. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 254–264.

Download PDF sample

Rated 4.88 of 5 – based on 45 votes