By Topic

Improving Kernel Density Classifier Using Corrective Bandwidth Learning with Smooth Error Loss Function

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Dwi Sianto Mansjur ; Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA ; Biing Hwang Juang

In this paper, we propose a corrective bandwidth learning algorithm for Kernel Density Estimation (KDE)-based classifiers. The objective of the corrective bandwidth learning algorithm is to minimize the expected error-rate. It utilizes a gradient descent technique to obtain the appropriate bandwidths. The proposed classifier is called the "Empirical Mixture Model" (EMM) classifier. Experiments were conducted on a set of multivariate multi-class classification problems with various data sizes. The proposed classifier has an error-rate closer to the true model compared to conventional KDE-based classifiers for both small and large data sizes. Additional experiments on standard machine learning datasets showed that the proposed bandwidth learning algorithm performed very well in gen-eral.

Published in:

Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on

Date of Conference:

11-13 Dec. 2008