Download e-book for kindle: AI*IA 2011: Artificial Intelligence Around Man and Beyond: by Stephen Grossberg (auth.), Roberto Pirrone, Filippo Sorbello

By Stephen Grossberg (auth.), Roberto Pirrone, Filippo Sorbello (eds.)

ISBN-10: 3642239536

ISBN-13: 9783642239533

ISBN-10: 3642239544

ISBN-13: 9783642239540

This publication constitutes the refereed lawsuits of the twelfth foreign convention of the Italian organization for synthetic Intelligence, AI*IA 2011, held in Palermo, Italy, in September 2011. The 31 revised complete papers offered including three invited talks and thirteen posters have been rigorously reviewed and chosen from fifty eight submissions. The papers are equipped in topical sections on desktop studying; allotted AI: robotics and MAS; theoretical matters: wisdom illustration and reasoning; making plans, cognitive modeling; ordinary language processing; and AI applications.

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Additional resources for AI*IA 2011: Artificial Intelligence Around Man and Beyond: XIIth International Conference of the Italian Association for Artificial Intelligence, Palermo, Italy, September 15-17, 2011. Proceedings

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U, xi ∈ X }, that is exploited to improve the quality of the classifier. In a practical context, unlabeled data can be acquired relatively easily, whereas labeling requires the Semi-Supervised Multiclass Kernel Machines with Probabilistic Constraints 23 expensive work of one or more supervisors, so that frequently we have u >> l. Unlabeled samples are drawn accordingly to the marginal distribution PX of P , and the Semi-Supervised framework attempts to incorporate them into the learning process in different ways.

TSA vs. state-of-the-art algorithms. As already pointed out, to compare TSA we considered the algorithms proposed by D’Alessio et al. [9], by Ruiz [13], and by Ceci and Malerba [7]. We used δ = 10−3 for TSA. Let us note that Ruiz uses the same threshold value for level 3 and level 4, whereas we let its algorithm to search on the entire space of thresholds. In so doing, the results in terms of utility functions cannot be worse than those calculated by means of the original algorithm. However, the running time is one order of magnitude greater than the original algorithm.

In a generic k-class classification problem, we want to infer the function c : X → Y, where Y is a set of labels. We indicate with yi ∈ Y the label associated to xi . Suppose that there is a probability distribution P on X × Y, according to which data are generated. In a Supervised classification problem, we have a labeled training set L of l pairs, L = {(xi , yi )|i = 1, . . , l, xi ∈ X , yi ∈ Y}, and the classifier is trained to estimate c(·) using the information in L. A labeled validation set V, if available, is used to tune the classifier parameters, whereas the generalization capabilities are evaluated on an out-of-sample test set T , in a typical inductive setting.

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AI*IA 2011: Artificial Intelligence Around Man and Beyond: XIIth International Conference of the Italian Association for Artificial Intelligence, Palermo, Italy, September 15-17, 2011. Proceedings by Stephen Grossberg (auth.), Roberto Pirrone, Filippo Sorbello (eds.)


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