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

By Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita

This e-book 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 awarded including five invited papers and an creation via the editors have been conscientiously reviewed and chosen from ninety eight submissions. The papers are equipped in topical sections on kernel-based studying, bayesian and statistical versions, PAC-learning, query-learning, inductive inference, language studying, studying and good judgment, studying from specialist recommendation, 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

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Recall that a statistic sL (D) is a sufficient statistic for learning a hypothesis h using a learning algorithm L applied to a data set D if there exists a procedure that takes sL (D) as input and outputs h [20]. For example, in the case of NBL, the frequency counts σ(vi |cj ) of the value vi of the attribute Ai given the class label cj in a training set D, and the frequency count σ(cj ) of the class label cj in a training set D completely summarize the information needed for constructing a Naive Bayes classifier from D, and thus, they constitute sufficient statistics for NBL.

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). After Haussler [75], we define a cut γi of an AVT Ti as a subset of nodes in Ti satisfying the following two properties: (1) For any leaf l ∈ Leaves(Ti ), either l ∈ γi or l is a descendent of a node n ∈ γi ; and (2) For any two nodes f, g ∈ γi , f is neither a descendent nor an ancestor of g.

INDUS has been used to assemble several data sets used in the exploration of protein sequence-structure-function relationships [44]. 3 Related Work on Data Integration Hull [49], Davidson et al. [50] and Eckman [51] survey alternative approaches to data integration. A wide range of approaches to data integration have been Algorithms and Software for Collaborative Discovery 27 considered including multi-database systems [52, 53, 54], mediator based approaches [55, 56, 57, 58, 59, 60, 61, 62]. Several data integration projects have focused specifically on integration of biological data [63, 64, 65, 66, 67].

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