Download e-book for iPad: Algorithmic Learning Theory: 16th International Conference, by Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita

By Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita

ISBN-10: 354029242X

ISBN-13: 9783540292425

ISBN-10: 3540316965

ISBN-13: 9783540316961

This publication constitutes the refereed court cases of the sixteenth foreign convention on Algorithmic studying conception, ALT 2005, held in Singapore in October 2005.

The 30 revised complete papers offered including five invited papers and an advent through the editors have been conscientiously reviewed and chosen from ninety eight submissions. The papers are prepared in topical sections on kernel-based studying, bayesian and statistical types, PAC-learning, query-learning, inductive inference, language studying, studying and common sense, studying from specialist suggestion, on-line studying, protective forecasting, and teaching.

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

Example text

Let Ψ = {ϕ1 , ϕ2 , · · · , ϕp } be a collection of user-defined mappings from data source taxonomies Oi to user taxonomies OU , respectively. A global cut Γ U in the user’s collection of taxonomies OU = {T1U , · · · , TnU } determines cuts {Γ1 , Γ2 , · · · , Γn } in the data source taxonomies, through the means of user-defined mappings Ψ . The abstract instance space defined by Γ U is denoted by IΓ U and is given by IΓU = ϕ1 (IΓ1 ) ∪ ϕ2 (IΓ2 )... ∪ ϕp (IΓn ). , TnU induces an instance space IOu = ∪Γ U IΓ U .

Each arc of the tree corresponds to a isa relationship over attribute values in the AVT. Thus, an AVT defines an abstraction hierarchy over values of an attribute. The set of abstract values at any given level in the tree Ti form a partition of the set of values at the next level (and hence, the set of primitive values of Ai ). , Undergrad, Grad, define a partition of attribute values that correspond to nodes at level 2 (and hence, a partition of all primitive values of the Student Status attribute).

Caragea et al. Fig. 6. General Framework for learning classifiers from semantically heterogeneous distributed data. The learner generates statistical queries expressed in terms of user ontology). These queries have to be mapped into queries expressed in terms of data source specific ontologies that can be executed directly on the data sources and the results combined and mapped into the answer to the query posed by the learner. framework to work with semantically heterogeneous distributed data sources by developing techniques for answering the statistical queries posed by the learner in terms of the learner’s ontology O using the heterogeneous data sources (where each data source Di has an associated ontology Oi ) (See Figure 6).

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Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings by Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita


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