A sparse Bayesian approach for joint feature selection and classifier learning

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

• Type: Journal
• Year: 2008
• Where: PAA
• Authors: Àgata Lapedriza, Santi Seguí, David Masip, Jordi Vitrià
• Keywords: Distributed And Parallel Computing, Global Loss Function, Joint Feature Selection, PAA 2008, Sparse Bayesian Approach