Applying emphasized soft targets for Gaussian Mixture Model based classification

When training machines classifiers, it is possible to replace hard classification targets by their emphasized soft versions so as to reduce the negative effects of using cost functions as approximations to misclassification rates. This emphasis has the same effect as sample editing methods which hav...

Full description

Saved in:
Bibliographic Details
Published in2008 International Multiconference on Computer Science and Information Technology pp. 131 - 136
Main Authors El Jelali, S., Lyhyaoui, A., Figueiras-Vidal, A.R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Boosting
Computer science
Convergence
Cost function
Error analysis
Information technology
Predictive models
Proposals
Signal processing
Signal processing algorithms
Online AccessGet full text

Cover

More Information
Summary:When training machines classifiers, it is possible to replace hard classification targets by their emphasized soft versions so as to reduce the negative effects of using cost functions as approximations to misclassification rates. This emphasis has the same effect as sample editing methods which have proved to be effective for improving classifiers performance. In this paper, we explore the effectiveness of using emphasized soft targets with generative models, such as Gaussian mixture models, that offer some advantages with respect to decision (prediction) oriented architectures, such as an easy interpretation and possibilities of dealing with missing values. Simulation results support the usefulness of the proposed approach to get better performance and show a low sensitivity to design parameters selection.
ISBN:8360810141
9788360810149
DOI:10.1109/IMCSIT.2008.4747229