]> BibSonomy publications for /author/Genkin http://www.bibsonomy.org/burst/author/Genkin BibSonomy BuRST Feed for /author/Genkin 2008-07-20T23:35:21+02:00 Organizacija, normirovanie i oplata truda na promyÿéslennych predprijatijach : uÿécebnik dlja vuzov importedhttp://www.bibsonomy.org/bibtex/227b3e6037eff0d946921e0e905db73fa/fbw fbw 2008-05-29T13:01:59+02:00 imported
Organizacija, normirovanie i oplata truda na promyÿéslennych predprijatijach : uÿécebnik dlja vuzov
{Boris Michajloviÿéc} Genkin (2003)
to imported by fbw on 2008-05-29 13:01:59 ]]> NORMAX, 389 S.MoskvaOrganizacija, normirovanie i oplata truda na promyÿéslennych predprijatijach : uÿécebnik dlja vuzov2003imported 2008-05-29 13:01:59.0 Large-Scale Bayesian Logistic Regression for Text Categorization IngentaConnect Large-Scale Bayesian Logistic Regression for Text Categorizationhttp://www.bibsonomy.org/bibtex/211830035a44e5db49d194a7f7a3f35ed/jhammerb jhammerb 2007-12-17T01:08:17+01:00 classification data_mining logistic regression text_mining
Large-Scale Bayesian Logistic Regression for Text Categorization
Alexander Genkin and David D. Lewis and David Madigan Technometrics 49 291-304(14) (August 2007)
to classification data_mining logistic regression text_mining by jhammerb on 2007-12-17 01:08:17 ]]> Technometrics291-304(14)Large-Scale Bayesian Logistic Regression for Text Categorization49August 2007classification data_mining logistic regression text_mining 2007-12-17 01:08:17.0Logistic regression analysis of high-dimensional data, such as natural language text, poses computational and statistical challenges. Maximum likelihood estimation often fails in these applications. We present a simple Bayesian logistic regression approach that uses a Laplace prior to avoid overfitting and produces sparse predictive models for text data. We apply this approach to a range of document classification problems and show that it produces compact predictive models at least as effective as those produced by support vector machine classifiers or ridge logistic regression combined with feature selection. We describe our model fitting algorithm, our open source implementations (BBR and BMR), and experimental results. Constructing informative prior distributions from domain knowledge in text classification. http://www.bibsonomy.org/bibtex/2635997d34b77c2dde18c229867257017/flawed flawed 2007-05-30T18:43:30+02:00 classification priors
Constructing informative prior distributions from domain knowledge in text classification.
Aynur A. Dayanik and David D. Lewis and David Madigan and Vladimir Menkov and Alexander Genkin SIGIR 493-500 (2006)
to classification priors by flawed on 2007-05-30 18:43:30 ]]> SIGIRconf/sigir/2006493-500Constructing informative prior distributions from domain knowledge in text classification.2006classification priors 2007-05-30 18:43:30.0 Supervised learning approaches to text classification are in practice often required to work with small and unsystematically collected training sets. The alternative to supervised learning is usually viewed to be building classifiers by hand, using a domain expert's understanding of which features of the text are related to the class of interest. This is expensive, requires a degree of sophistication about linguistics and classification, and makes it difficult to use combinations of weak predictors. We propose instead combining domain knowledge with training examples in a Bayesian framework. Domain knowledge is used to specify a prior distribution for the parameters of a logistic regression model, and labeled training data is used to produce a posterior distribution, whose mode we take as the final classifier. We show on three text categorization data sets that this approach can rescue what would otherwise be disastrously bad training situations, producing much more effective classifiers. Constructing informative prior distributions from domain knowledge in text classification. dblphttp://www.bibsonomy.org/bibtex/2635997d34b77c2dde18c229867257017/dblp dblp 2006-08-30T00:00:00+02:00 dblp
Constructing informative prior distributions from domain knowledge in text classification.
Aynur A. Dayanik and David D. Lewis and David Madigan and Vladimir Menkov and Alexander Genkin SIGIR 493-500 (2006)
to dblp by dblp on 2006-08-30 00:00:00 ]]> SIGIRconf/sigir/2006493-500Constructing informative prior distributions from domain knowledge in text classification.2006dblp 2006-08-30 00:00:00.0 The method of LP - search for the optimization of multiparametric and multicriterial problems in engineering design. dblphttp://www.bibsonomy.org/bibtex/2dbf75369e074de7c58c4b3d958de8fa5/dblp dblp 2004-01-22T00:00:00+01:00 dblp
The method of LP - search for the optimization of multiparametric and multicriterial problems in engineering design.
I. I. Artobolevski and W. M. Grinkewitch and I. M. Sobol and M. D. Genkin and W. I. Sergeyev and R. B. Statnikov Computer-Aided Design 7 160-162 (1975)
to dblp by dblp on 2004-01-22 00:00:00 ]]> Computer-Aided Design3160-162The method of LP - search for the optimization of multiparametric and multicriterial problems in engineering design.71975dblp 2004-01-22 00:00:00.0 Set covering submodular maximization: An optimal algorithm for data mining in bioinformatics and medical informatics. dblphttp://www.bibsonomy.org/bibtex/2107a79de145242a3abf097f78f542450/dblp dblp 2003-08-20T00:00:00+02:00 dblp
Set covering submodular maximization: An optimal algorithm for data mining in bioinformatics and medical informatics.
Alexander Genkin and Casimir A. Kulikowski and Ilya B. Muchnik Journal of Intelligent and Fuzzy Systems 12 5-17 (2002)
to dblp by dblp on 2003-08-20 00:00:00 ]]> Journal of Intelligent and Fuzzy Systems15-17Set covering submodular maximization: An optimal algorithm for data mining in bioinformatics and medical informatics.122002dblp 2003-08-20 00:00:00.0