By Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita
This booklet constitutes the refereed complaints of the sixteenth overseas convention on Algorithmic studying thought, ALT 2005, held in Singapore in October 2005.
The 30 revised complete papers provided including five invited papers and an creation via the editors have been conscientiously reviewed and chosen from ninety eight submissions. The papers are equipped in topical sections on kernel-based studying, bayesian and statistical versions, PAC-learning, query-learning, inductive inference, language studying, studying and common sense, studying from specialist suggestion, on-line studying, protective forecasting, and teaching.
Read Online or Download Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings PDF
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Additional info for Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings
Now consider a diﬀerent statistical query rOU : What fraction of the students in the two data sources are sophomores? The answer to this query is not as straightforward as the answer to the previous query q OU . This is due to the fact that the Student Status of student records in data source D2 are only partially Algorithms and Software for Collaborative Discovery 29 speciﬁed [73, 74] with respect to the ontology O. Consequently, we can never know the precise fraction of students that are Sophomores based on the information available in the two data sources.
Agents that learn from distributed dynamic data sources. : Learning support vector machine classiﬁers from distributed data. extended abstract. In: Proceedings of the 22nd National Conference on Artiﬁcial Intelligence (AAAI 2005).
Eﬃcient noise-tolerant learning from statistical queries. : A framework for learning from distributed data using suﬃcient statistics and its application to learning decision trees. : Decision tree induction from distributed heterogeneous autonomous data sources. : Agents that learn from distributed dynamic data sources. : Learning support vector machine classiﬁers from distributed data. extended abstract. In: Proceedings of the 22nd National Conference on Artiﬁcial Intelligence (AAAI 2005).