By Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita
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.
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
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  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  propose an algorithm for learning decision tree classiﬁers from vertically fragmented distributed data. Kargupta et al.  describe an algorithm for learning decision trees from vertically fragmented distributed data using a technique proposed by Mansour  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.
Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings by Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita