TY - GEN
T1 - An analysis of logistic models
T2 - 7th SIAM International Conference on Data Mining
AU - Banerjee, Arindam
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - Logistic models are arguably one of the most widely used data analysis techniques. In this paper, we present analyses focussing on two important aspects of logistic models-its relationship with exponential family based generative models, and its performance in online and potentially adversarial settings. In particular, we present two new theoretical results on logistic models focusing on the above two aspects. First, we establish an exact connection between logistic models and exponential family based generative models, resolving a long-standing ambiguity over their relationship. Second, we show that online Bayesian logistic models are competitive to the best batch models, even in potentially adversarial settings. Further, we discuss relevant connections of our analysis to the literature on integral transforms, and also present a new optimality result for Bayesian models. The analysis makes a strong case for using logistic models and partly explains the success of such models for a wide range of practical problems.
AB - Logistic models are arguably one of the most widely used data analysis techniques. In this paper, we present analyses focussing on two important aspects of logistic models-its relationship with exponential family based generative models, and its performance in online and potentially adversarial settings. In particular, we present two new theoretical results on logistic models focusing on the above two aspects. First, we establish an exact connection between logistic models and exponential family based generative models, resolving a long-standing ambiguity over their relationship. Second, we show that online Bayesian logistic models are competitive to the best batch models, even in potentially adversarial settings. Further, we discuss relevant connections of our analysis to the literature on integral transforms, and also present a new optimality result for Bayesian models. The analysis makes a strong case for using logistic models and partly explains the success of such models for a wide range of practical problems.
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U2 - 10.1137/1.9781611972771.19
DO - 10.1137/1.9781611972771.19
M3 - Conference contribution
AN - SCOPUS:55349140556
SN - 9780898716306
T3 - Proceedings of the 7th SIAM International Conference on Data Mining
SP - 204
EP - 215
BT - Proceedings of the 7th SIAM International Conference on Data Mining
PB - Society for Industrial and Applied Mathematics Publications
Y2 - 26 April 2007 through 28 April 2007
ER -