A group focusing on the theory and application of machine learning with a statistical flavour, e.g., support vector machines and kernel methods, Bayesian inference, analysis of risk, etc. http://www.citeulike.org/groupfunc/3808 CiteULike.org en-gb Copyright © 2004-2007 Oversity Limited http://www.citeulike.org/profile/achaemenes 2009-12-28T06:14:51+00:00 http://www.citeulike.org/group/3808/article/6440657 User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is scarce or non-existent. We review existing techniques for dealing with this problem and propose a novel alternative based on a principled statistical decision-making view point. We examine the technique on a toy problem and validate it on complex real-world data from an RFID based access control system. The results indicate that it can significantly outperform the classical world model approach. The method could be more generally useful in other decision-making scenarios where there is a lack of adversary data. 2009-12-26T17:09:21+00:00 http://www.citeulike.org/profile/zhengyun 2009-12-22T17:39:48+00:00 http://www.citeulike.org/group/3808/article/6416060 We provide yet another proof of the existence of calibrated forecasters; it has two merits. First, it is valid for an arbitrary finite number of outcomes. Second, it is short and simple and it follows from a direct application of Blackwell's approachability theorem to carefully chosen vector-valued payoff function and convex target set. Our proof captures the essence of existing proofs based on approachability (e.g., the proof by Foster, 1999 in case of binary outcomes) and highlights the intrinsic connection between approachability and calibration. 2009-12-22T04:16:24+00:00