Download Algorithmic Learning Theory: 16th International Conference, by Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita PDF

By Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita

This publication 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 awarded including five invited papers and an creation by means of the editors have been rigorously reviewed and chosen from ninety eight submissions. The papers are equipped in topical sections on kernel-based studying, bayesian and statistical types, PAC-learning, query-learning, inductive inference, language studying, studying and good judgment, studying from specialist suggestion, on-line studying, shielding forecasting, and teaching.

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Extra info for Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings

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We have completed the implementation of a working prototype of the INDUS system to enable users with some familiarity with the relevant data sources to rapidly and flexibly assemble data sets from multiple data sources and to query these data sets. This can be done by specifying a user ontology, simple semantic mappings between data source specific ontologies and the user ontolgy and queries - all without having to write any code. html) includes support for: (a) Import and reuse of selected fragments of existing ontologies and editing of ontologies [47].

Consequently, we can devise a strategy for computing h from the data D through some combination of refinement and composition operations starting with an initial hypothesis (or an initial set of hypotheses). When the learner’s access to data sources is subject to constraints Z, the resulting plan for information extraction has to be executable without violating the constraints Z. The exactness of the algorithm Ld for learning from distributed data relative to its centralized counterpart, which requires access to the complete data set D follows from the correctness (soundness) of the query decomposition and answer composition procedure.

We have devised provably sound strategies for gathering the necessary statistics from distributed data sets, thereby obtaining distributed decision tree learning algorithms that are provably exact relative to their centralized counterparts [21]. This approach to learning decision trees from distributed data provides an effective way to learn classifiers in scenarios in which the distributed data sources provide only statistical summaries of the data and the set of unique keys on demand but prohibit access to data instances.

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