New PDF release: Algorithmic Learning Theory: 16th International Conference,

By Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita

ISBN-10: 354029242X

ISBN-13: 9783540292425

ISBN-10: 3540316965

ISBN-13: 9783540316961

This e-book constitutes the refereed complaints of the sixteenth foreign convention on Algorithmic studying conception, 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 geared up 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 professional suggestion, on-line studying, protective forecasting, and teaching.

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By Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita

ISBN-10: 354029242X

ISBN-13: 9783540292425

ISBN-10: 3540316965

ISBN-13: 9783540316961

This e-book constitutes the refereed complaints of the sixteenth foreign convention on Algorithmic studying conception, 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 geared up 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 professional suggestion, on-line studying, protective forecasting, and teaching.

Show description

Read or Download Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings PDF

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

Example text

Fig. 4. INDUS (Intelligent Data Understanding System) for Information Integration from Heterogeneous, Distributed, Autonomous Information Sources. D1 , D2 , D3 are data sources with associated ontologies O1 , O2 , O3 and O is a user ontology. Queries posed by the user are answered by a query answering engine in accordance with the mappings between user ontology and the data source ontologies, specified using a userfriendly editor. e. a collection of inter-related tables. , the schema of the data source).

2 [20] offers a general framework for the design of algorithms for learning from distributed data that is provably exact with respect to its centralized counterpart. Central to our approach is a clear separation of concerns between hypothesis construction and extraction of sufficient statistics from data, making it possible to explore the use of sophisticated techniques for query optimization that yield optimal plans for gathering sufficient statistics from distributed data 22 D. Caragea et al. , execution of user supplied procedures).

A third important limitation of the ensemble classifier approach to learning from distributed data is the lack of strong guarantees concerning accuracy of the resulting hypothesis relative to the hypothesis obtained in the centralized setting. Bhatnagar and Srinivasan [40] propose an algorithm for learning decision tree classifiers from vertically fragmented distributed data. Kargupta et al. [41] describe an algorithm for learning decision trees from vertically fragmented distributed data using a technique proposed by Mansour [42] for approximating a decision tree using Fourier coefficients corresponding to attribute combinations whose size is at most logarithmic in the number of nodes in the tree.

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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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