New PDF release: Algorithmic Learning Theory: 15th International Conference,

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

ISBN-10: 1846289564

ISBN-13: 9781846289569

Algorithmic studying idea is arithmetic approximately machine courses which research from event. This comprises huge interplay among quite a few mathematical disciplines together with idea of computation, data, and c- binatorics. there's additionally significant interplay with the sensible, empirical ?elds of computer and statistical studying during which a critical goal is to foretell, from earlier info approximately phenomena, necessary beneficial properties of destiny facts from a similar phenomena. The papers during this quantity conceal a extensive variety of issues of present examine within the ?eld of algorithmic studying concept. we've divided the 29 technical, contributed papers during this quantity into 8 different types (corresponding to 8 classes) re?ecting this large variety. the types featured are Inductive Inf- ence, Approximate Optimization Algorithms, on-line series Prediction, S- tistical research of Unlabeled info, PAC studying & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. lower than we supply a short assessment of the ?eld, putting every one of those themes within the common context of the ?eld. Formal versions of automatic studying re?ect numerous features of the big variety of actions that may be seen as studying. A ?rst dichotomy is among viewing studying as an inde?nite technique and viewing it as a ?nite job with a de?ned termination. Inductive Inference types specialize in inde?nite studying procedures, requiring in basic terms eventual luck of the learner to converge to a passable conclusion.

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

Example text

Between carrier(X) and carrier(M). By now, we are able to define the covers relation for Bayesian logic programs. A set of Bayesian logic program together with the background theory induces a Bayesian network. The random variables A of the Bayesian network are the Bayesian ground atoms in the least Herbrand model I of the annotated logic program. A Bayesian ground atom, say carrier(alan), influences another Bayesian ground atom, say carrier(betsy), if and only if there exists a Bayesian clause such that 1.

11] T. Dietterich, L. Getoor, and K. Murphy, editors. Working Notes of the ICML2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-04), 2004. [12] A. Eisele. Towards probabilistic extensions of contraint-based grammars. In J. Dörne, editor, Computational Aspects of Constraint-Based Linguistics Decription-II. B, 1994. [13] J. Fürnkranz. Separate-and-Conquer Rule Learning. Artificial Intelligence Review, 13(1):3–54, 1999. [14] L. Getoor and D. Jensen, editors.

In this paper, we choose Bayesian logic programs [21] as the probabilistic logic programming system because Bayesian logic programs combine Bayesian networks [37], which represent probability distributions over propositional interpretations, with definite clause logic. Furthermore, Bayesian logic programs have already been employed for learning. The idea underlying Bayesian logic programs is to view ground atoms as random variables that are defined by the underlying definite clause programs. Furthermore, two types of predicates are distinguished: deterministic and probabilistic ones.

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Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings by Ayumi Shinohara (auth.), Shoham Ben-David, John Case, Akira Maruoka (eds.)


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