By Stephen Grossberg (auth.), Roberto Pirrone, Filippo Sorbello (eds.)
This ebook constitutes the refereed court cases of the twelfth foreign convention of the Italian organization for man made Intelligence, AI*IA 2011, held in Palermo, Italy, in September 2011. The 31 revised complete papers provided including three invited talks and thirteen posters have been rigorously reviewed and chosen from fifty eight submissions. The papers are prepared in topical sections on desktop studying; dispensed AI: robotics and MAS; theoretical concerns: wisdom illustration and reasoning; making plans, cognitive modeling; average 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
Dataset XM2VTS Subjects 295 Total Images 2360 Data Split Image IDs (per subject) Total Images Training S =L∪U 1, 2, 3, 4 1180 Validation V 5,6 590 Test T 7,8 590 We compared the proposed Multiclass Classiﬁer with Probabilistic Constraints (MC-PC) with an unconstrained OVA Multiclass Support Vector Machines (MSVM) that it is one of the most popular approaches and it has been shown to be as accurate as the most of the other existing techniques . Experiments have also been performed using a K-Nearest Neighbors (KNN) classiﬁer with Euclidean distance, since it is frequently used in face recognition experiments.
Each pair of rows in Table 1 reports the comparison in terms of the time spent to complete each calibrate step (tlev4 . . tlev1 ), together with the corresponding F1 . Results clearly show that the cumulative running time for RBF tends to rapidly become intractable3, while the values of F1 are comparable. TSA vs. state-of-the-art algorithms. As already pointed out, to compare TSA we considered the algorithms proposed by D’Alessio et al. , by Ruiz , and by Ceci and Malerba . We used δ = 10−3 for TSA.
K. Clearly, for xi ∈ L, this information is implicitly embedded on the targets yij , j = 1, . . , k. As a consequence, a hypothetic perfect ﬁtting of labeled points would fulﬁll Eq. 4 ∀x ∈ L. On the other hand, each fj is requested to be smooth in the RKHS, and a perfect ﬁtting is generally not achieved. Interestingly, Eq. 4 gives us a basic domain information on the problem that is supposed to hold in the entire input space. We exploit this information on the relationship among the functions fj , j = 1, .