By Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita
This publication constitutes the refereed lawsuits of the sixteenth foreign convention on Algorithmic studying conception, ALT 2005, held in Singapore in October 2005.
The 30 revised complete papers provided including five invited papers and an creation by way of the editors have been rigorously reviewed and chosen from ninety eight submissions. The papers are prepared 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 professional suggestion, on-line studying, protecting 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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This publication constitutes the refereed complaints of the sixteenth foreign 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 through the editors have been conscientiously reviewed and chosen from ninety eight submissions.
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Additional info for Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings
1997]. More precisely, the task of AVT-NBL is to construct a Naive Bayes classiﬁer for assigning an unlabeled instance x ∈ IO to its most probable class cMAP (x). As in the case of NBL, we assume that each attribute is independent of the other attributes given the class. A Naive Bayes classiﬁer deﬁned on the instance space IO is completely speciﬁed by a set of class conditional probabilities for each value of each attribute. Suppose we denote the table of class conditional probabilities associated with values in γi by CP T (γi ).
While this work suggests ways to aggregate statistics so as to minimize information loss, it does not address the problem of learning from AVT and partially speciﬁed data. The problem of learning classiﬁers from AVT and partially speciﬁed data was formulated and solved in the case of decision tree classiﬁers by Zhang and Honavar  and in the case of Naive Bayes classiﬁers by Zhang and Honavar . Development of approaches to exploit abstractions over attribute values and class labels to optimally exploit partially speciﬁed data.
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). INDUS makes explicit the (sometimes implicit) ontologies associated with data sources. This allows the speciﬁcation of semantic correspondences between data sources  which can be expressed in ontology-extended relational algebra (independently developed by ).