A sparse Bayesian approach for joint feature selection and classifier learning

13 years 4 months ago

Download www.cvc.uab.es

Abstract In this paper we present a new method for Joint Feature Selection and Classifier Learning (JFSCL) using a sparse Bayesian approach. These tasks are performed by optimizing a global loss function that includes a term associated with the empirical loss and another one representing a fea

Àgata Lapedriza, Santi Seguí, David

Real-time Traffic

Distributed And Parallel Computing | Global Loss Function | Joint Feature Selection | PAA 2008 | Sparse Bayesian Approach |

claim paper

» Learning sparse models for a dynamic Bayesian network classifier of protein secondary stru...

» Expectation Propagation for Bayesian Multitask Feature Selection

» Optimal Feature Selection for Subspace Image Matching

» Learning Bayesian Classifiers from GeneExpression MicroArray Data

» Predictive automatic relevance determination by expectation propagation

» Graphic Symbol Recognition Using Graph Based Signature and Bayesian Network Classifier

» Learning with Mixtures of Trees

» Learning Multicategory Classification in Bayesian Framework

Post Info
More Details (n/a)

Added	14 Dec 2010
Updated	14 Dec 2010
Type	Journal
Year	2008
Where	PAA
Authors	Àgata Lapedriza, Santi Seguí, David Masip, Jordi Vitrià

Comments (0)

Sciweavers

A sparse Bayesian approach for joint feature selection and classifier learning

Distributed And Parallel Computing | Global Loss Function | Joint Feature Selection | PAA 2008 | Sparse Bayesian Approach |

Explore & Download

Productivity Tools

Document Tools

Image Tools

Sciweavers