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 conception is arithmetic approximately desktop courses which research from adventure. This contains significant interplay among numerous mathematical disciplines together with idea of computation, facts, and c- binatorics. there's additionally huge interplay with the sensible, empirical ?elds of computer and statistical studying within which a significant target is to foretell, from earlier information approximately phenomena, priceless beneficial properties of destiny information from an analogous phenomena. The papers during this quantity hide a wide diversity of issues of present learn within the ?eld of algorithmic studying idea. now we have 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. less than we supply a quick evaluate of the ?eld, putting each one of those themes within the normal context of the ?eld. Formal types of computerized studying re?ect a number of features of the big variety of actions that may be considered as studying. A ?rst dichotomy is among viewing studying as an inde?nite strategy 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.

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

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Sammut, editors, Proceedings of the Twelfth International Conference on Inductive Logic Prgramming (ILP-02), volume 2583 of LNCS, pages 198–206, Sydney, Australia, 2002. Springer. [34] S. Muggleton and C. Feng. Efficient induction of logic programs. In S. Muggleton, editor, Inductive Logic Programming, pages 281–298. Acadamic Press, 1992. [35] L. Ngo and P. Haddawy. Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science, 171(1–2):147–177, 1997. [36] S.

Muta(M) bond(M, A, 1), muta(M) bond(M, A, 1), atom(M, A, c, 22, _), etc. For each refinement we then compute the maximum-likelihood parameters and The refinement that scores best, say is then considered for further refinement and the refinement process terminates when Preliminary results with a prototype implementation are promising. 4 Learning from Probabilistic Interpretations SCOOBY [22,20,23] is a greedy hill-climbing approach for learning Bayesian logic programs. SCOOBY takes the initial Bayesian logic program as starting point and computes the parameters maximizing Then, refinement operators generalizing respectively specializing H are used to to compute all legal neighbours of H in the hypothesis space, see Figure 1.

De Abstract. Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed. In the present paper, we start from inductive logic programming and sketch how it can be extended with probabilistic methods. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be used to learn different types of probabilistic representations.

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