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

By David Sh. B. (Ed), Case J. (Ed), Maruoka A. (Ed)

ISBN-10: 3540302158

ISBN-13: 9783540302155

This e-book constitutes the refereed lawsuits of the fifteenth overseas convention on Algorithmic studying idea, ALT 2004, held in Padova, Italy in October 2004.The 29 revised complete papers provided including five invited papers and three instructional summaries have been rigorously reviewed and chosen from ninety one submissions. The papers are equipped in topical sections on inductive inference, PAC studying and boosting, statistical supervised studying, on-line series studying, approximate optimization algorithms, good judgment dependent studying, and question and reinforcement studying.

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

Example text

However, to deal with missing information, Shapiro employs a clever strategy: MIS queries the user for missing information by asking her for Probabilistic Inductive Logic Programming 23 the truth-value of facts. The answers to these queries allow MIS to reconstruct the trace or the proof of the positive examples. Inspired by Shapiro, we define the learning from proofs setting. Definition 3. t. the background theory B if and only if is a proof-tree for At this point, there exist various possible forms of proof-trees.

When learning stochastic logic programs from entailment, the example clauses must be entailed by the logic program, and when learning Bayesian logic programs, the interpretation must be a model of the logic program. At this point, it is interesting to observe that in the learning from entailment setting the examples do have to be covered in the logical sense when using the setting combining FOIL and naïve Bayes, whereas using the stochastic logic programs all examples must be logically entailed.

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. Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-03), 2003. [15] P. Haddawy. Generating Bayesian networks from probabilistic logic knowledge bases. In R. López de Mántaras and D. Poole, editors, Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-1994), pages 262–269, Seattle, Washington, USA, 1994.

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Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004, Proceedings by David Sh. B. (Ed), Case J. (Ed), Maruoka A. (Ed)

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