Results 1  10
of
4,113,714
Scalable training of L1regularized loglinear models
 In ICML ’07
, 2007
"... The lbfgs limitedmemory quasiNewton method is the algorithm of choice for optimizing the parameters of largescale loglinear models with L2 regularization, but it cannot be used for an L1regularized loss due to its nondifferentiability whenever some parameter is zero. Efficient algorithms have ..."
Abstract

Cited by 169 (4 self)
 Add to MetaCart
The lbfgs limitedmemory quasiNewton method is the algorithm of choice for optimizing the parameters of largescale loglinear models with L2 regularization, but it cannot be used for an L1regularized loss due to its nondifferentiability whenever some parameter is zero. Efficient algorithms
Stochastic Gradient Descent Training for L1regularized Loglinear Models with Cumulative Penalty
"... Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework is attractive because it often requires much less training time in practice than batch training algorithms. However, L1re ..."
Abstract

Cited by 39 (0 self)
 Add to MetaCart
much more quickly than a stateoftheart quasiNewton method for L1regularized loglinear models. 1
Parsing the WSJ using CCG and loglinear models
 In Proceedings of the 42nd Meeting of the ACL
, 2004
"... This paper describes and evaluates loglinear parsing models for Combinatory Categorial Grammar (CCG). A parallel implementation of the LBFGS optimisation algorithm is described, which runs on a Beowulf cluster allowing the complete Penn Treebank to be used for estimation. We also develop a new eff ..."
Abstract

Cited by 187 (22 self)
 Add to MetaCart
This paper describes and evaluates loglinear parsing models for Combinatory Categorial Grammar (CCG). A parallel implementation of the LBFGS optimisation algorithm is described, which runs on a Beowulf cluster allowing the complete Penn Treebank to be used for estimation. We also develop a new
LogLinear Models
, 2004
"... This is yet another introduction to loglinear (“maximum entropy”) models for NLP practitioners, in the spirit of Berger (1996) and Ratnaparkhi (1997b). The derivations here are similar to Berger’s, but more details are filled in and some errors are corrected. I do not address iterative scaling (Dar ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Darroch and Ratcliff, 1972), but rather give derivations of the gradient and Hessian of the dual objective function (conditional likelihood). Note: This is a draft; please contact the author if you have comments, and do not cite or circulate this document. 1 Loglinear Models Loglinear models 1 have
Conditional LogLinear Structures for LogLinear
"... A loglinear modelling will take quite a long time if the data involves many variables and if we try to deal with all the variables at once. Fienberg and Kim (1999) investigated the relationship between loglinear model and its conditional, and we will show how this relationship is employed to make ..."
Abstract
 Add to MetaCart
A loglinear modelling will take quite a long time if the data involves many variables and if we try to deal with all the variables at once. Fienberg and Kim (1999) investigated the relationship between loglinear model and its conditional, and we will show how this relationship is employed
Learning probabilistic relational models
 In IJCAI
, 1999
"... A large portion of realworld data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
Abstract

Cited by 619 (31 self)
 Add to MetaCart
of the dependency structure in a model. Moreover, we show how the learning procedure can exploit standard database retrieval techniques for efficient learning from large datasets. We present experimental results on both real and synthetic relational databases. 1
Generalized Additive Models
, 1984
"... Likelihood based regression models, such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariate effects. We introduce the Local Scotinq procedure which replaces the liner form C Xjpj by a sum of smooth functions C Sj(Xj) ..."
Abstract

Cited by 2413 (46 self)
 Add to MetaCart
Likelihood based regression models, such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariate effects. We introduce the Local Scotinq procedure which replaces the liner form C Xjpj by a sum of smooth functions C Sj
Limma: linear models for microarray data
 Bioinformatics and Computational Biology Solutions using R and Bioconductor
, 2005
"... This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."
Abstract

Cited by 759 (13 self)
 Add to MetaCart
This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents
Exploiting Generative Models in Discriminative Classifiers
 In Advances in Neural Information Processing Systems 11
, 1998
"... Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often resu ..."
Abstract

Cited by 538 (11 self)
 Add to MetaCart
Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often
LatticeBased Access Control Models
, 1993
"... The objective of this article is to give a tutorial on latticebased access control models for computer security. The paper begins with a review of Denning's axioms for information flow policies, which provide a theoretical foundation for these models. The structure of security labels in the ..."
Abstract

Cited by 1485 (56 self)
 Add to MetaCart
The objective of this article is to give a tutorial on latticebased access control models for computer security. The paper begins with a review of Denning's axioms for information flow policies, which provide a theoretical foundation for these models. The structure of security labels
Results 1  10
of
4,113,714