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Abstract: This paper presents a tutorial introduction to the logistic function as a statistical object. Beyond the discussion of the whys and wherefores of the logistic function, I also hope to illuminate the general distinction between the "generative/causal/class-conditional" and the "discriminative/diagnostic/ predictive" directions for the modeling of data. Crudely put, the belief network community has tended to focus on the former while the neural network community has tended to focus on the latter... (Update)
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...functions became so popular. Except for some neurobiological inspirations sigmoids may also be justified from a statistical point of view [2, 39]. Consider a classification problem in N dimensions with two classes described by Gaussian distributions with equal covariance...
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BibTeX entry: (Update)
Jordan, M. I. (1995) "Why the logistic function? A tutorial discussion on probabilities and neural networks," Technical Report. http://citeseer.ist.psu.edu/jordan95why.html More
@misc{ jordan95why,
author = "M. Jordan",
title = "Why the logistic function? A tutorial discussion on probabilities and neural
networks",
text = "Jordan, M. I. (1995) Why the logistic function? A tutorial discussion on
probabilities and neural networks, Technical Report.",
year = "1995",
url = "citeseer.ist.psu.edu/jordan95why.html" }
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