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Logistic regression...

by J. Mario Vargas , 2005
"... from logistic regression ..."
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from logistic regression

The group Lasso for logistic regression

by Lukas Meier, Sara Van De Geer, Peter Bühlmann, Eidgenössische Technische Hochschule Zürich - Journal of the Royal Statistical Society, Series B , 2008
"... Summary. The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic regressi ..."
Abstract - Cited by 276 (11 self) - Add to MetaCart
Summary. The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic

Logistic regression

by Dirk Speelman
"... This text offers an introduction to binary logistic regression, a confirmatory technique for statistically modelling the effect of one or several predictors on a binary response variable. It is explained why logistic regression is exceptionally well suited for the comparison of near synonyms in corp ..."
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This text offers an introduction to binary logistic regression, a confirmatory technique for statistically modelling the effect of one or several predictors on a binary response variable. It is explained why logistic regression is exceptionally well suited for the comparison of near synonyms

Additive Logistic Regression: a Statistical View of Boosting

by Jerome Friedman, Trevor Hastie, Robert Tibshirani - Annals of Statistics , 1998
"... Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input dat ..."
Abstract - Cited by 1750 (25 self) - Add to MetaCart
be viewed as an approximation to additive modeling on the logistic scale using maximum Bernoulli likelihood as a criterion. We develop more direct approximations and show that they exhibit nearly identical results to boosting. Direct multi-class generalizations based on multinomial likelihood are derived

On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes

by Andrew Y. Ng, Michael I. Jordan , 2001
"... We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is i ..."
Abstract - Cited by 520 (8 self) - Add to MetaCart
We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size

Maximum-Margin Logistic Regression

by Jason D. M. Rennie , 2005
"... The Regularized Logistic Regression (RLR) minimization objective is ..."
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The Regularized Logistic Regression (RLR) minimization objective is

logistic regression

by Konstantinos Fokianos , 2007
"... Abstract: Inference based on the penalized density ratio model is proposed and studied. The model under consideration is specified by assuming that the log–likelihood function of two unknown densities is of some parametric form. The model has been extended to cover multiple samples problems while it ..."
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Abstract: Inference based on the penalized density ratio model is proposed and studied. The model under consideration is specified by assuming that the log–likelihood function of two unknown densities is of some parametric form. The model has been extended to cover multiple samples problems while its theoretical properties have been investigated using large sample theory. A main application of the density ratio model is testing whether two, or more, distributions are equal. We extend these results by arguing that the penalized maximum empirical likelihood estimator has less mean square error than that of the ordinary maximum likelihood estimator, especially for small samples. In fact, penalization resolves any existence problems of estimators and a modified Wald type test statistic can be employed for testing equality of the two distributions. A limited simulation study supports further the theory.

Logistic regression

by Beam-column Connections, Beam Yielding , 2010
"... Failure initiation Joint shear failure ..."
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Failure initiation Joint shear failure

Logistic Regression in

by T Chris Schwiegelshohn
"... ch ni ca lR ep or ..."
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ch ni ca lR ep or

Privacy-preserving logistic regression

by Kamalika Chaudhuri, Claire Monteleoni
"... This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases. We focus on privacy-preserving logistic regression. First we apply an idea of Dwork et al. [7] to design a privacy-preserving logistic regression algorithm. Th ..."
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This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases. We focus on privacy-preserving logistic regression. First we apply an idea of Dwork et al. [7] to design a privacy-preserving logistic regression algorithm
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