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

By Sanjay Jain, Hans Ulrich Simon, Etsuji Tomita

This booklet 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 advent through the editors have been rigorously 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 professional suggestion, on-line studying, protective forecasting, and teaching.

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

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39] propose an ensemble of classifiers approach to learning from horizontally fragmented distributed data which essentially involves learning separate classifiers from each data set and combining them typically using a weighted voting scheme. This requires gathering a subset of data from each of the data sources at a central site to determine the weights to be assigned to the individual hypotheses (or shipping the ensemble of classifiers and associated weights to the individual data sources where they can be executed on local data to set the weights).

45]. The design of INDUS [10, 11, 44] was necessitated by the lack of publicly available data integration platforms that could be used as a basis for learning classifiers from semantically heterogeneous distributed data. INDUS draws on much of the existing literature on data integration and hence shares some of the features of existing data integration platforms. 2). 4 Knowledge Aquisition from Semantically Heterogeneous Distributed Data The stage is now set for developing sound approaches to learning from semantically heterogeneous, distributed data (See Figure 6).

Therefore, the task of the Naive Bayes Learner (NBL) is to estimate the class probabilities p(cj ) and the class conditional probabilities p(vi |cj ), for all classes cj ∈ C and for all attribute values vi ∈ dom(Ai ). These probabilities can be estimated from a training set D using standard probability estimation methods [1] based on relative frequency counts. We denote by σ(vi |cj ) the frequency count of a value vi of the attribute Ai given the class label cj , and by σ(cj ) the frequency count of the class label cj in a training set D.

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