Group Statistical Machine Learning
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
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achaemenes asked to join the group
http://www.citeulike.org/profile/achaemenes
2009-12-28T06:14:51+00:00
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olethros posted Statistical Decision Making for Authentication and Intrusion Detection
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
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zhengyun asked to join the group
http://www.citeulike.org/profile/zhengyun
2009-12-22T17:39:48+00:00
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mdreid posted A Geometric Proof of Calibration
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