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This ebook 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 awarded including three invited talks and thirteen posters have been rigorously reviewed and chosen from fifty eight submissions. The papers are geared up in topical sections on laptop studying; dispensed AI: robotics and MAS; theoretical matters: wisdom illustration and reasoning; making plans, cognitive modeling; traditional 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
Comparison of face veriﬁcation results on the XM2VTS database. In: Int. Conf. on Pattern Recognition, vol. 4, pp. 858–863. IEEE Computer Society, Los Alamitos (2000) 9. : Laplacian Support Vector Machines Trained in the Primal. Journal of Machine Learning Research 12, 1149–1184 (2011) 10. : XM2VTSDB: The Extended M2VTS Database. In: Proc. of the Int. Conf. on Audio and Video-based Biometric Person Authentication, pp. 72–79 (1999) 11. : Support vector machines applied to face recognition. Advances in Neural Information Processing Systems, 803–809 (1999) 12.
Laplacian Support Vector Machines Trained in the Primal. Journal of Machine Learning Research 12, 1149–1184 (2011) 10. : XM2VTSDB: The Extended M2VTS Database. In: Proc. of the Int. Conf. on Audio and Video-based Biometric Person Authentication, pp. 72–79 (1999) 11. : Support vector machines applied to face recognition. Advances in Neural Information Processing Systems, 803–809 (1999) 12. : Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. Advances in Kernel Methods Support Vector Learning, 61–74 (2000) 13.
Semi-Supervised Multiclass Kernel Machines with Probabilistic Constraints 25 label ﬁtting with a squared loss V (fj (xi ), yij ) = (fj (xi ) − yij )2 . Note that using a hinge loss leads to the same classiﬁcation accuracies, as investigated in , and it would not make any substantial diﬀerences with respect to the selected V due to the nature of the constraints that we will introduce in the following (that will enforce fj in [0, 1]). In its unconstrained and fully Supervised formulation, the OVA scheme does not guarantee that the output values f1 (x), .