Paper Info
Title
Incremental learning of mixture models for simultaneous estimation of class distribution and inter-class decision boundaries
Abstract
In this paper, we propose a novel design of high performance Bayes classifier from a small number of observations. The two main challenges to obtain the classifier are the lack of the true functional form of the class-conditional density and the lack of enough data to estimate the parameters of the classifiers. Incremen- tal learning of Gaussian Mixture Model (GMM) is used to mitigate the lack of the true functional form. More- over, the classifier uses the training samples from all classes to evaluate the goodness of a particular mixture to be used as the classifier for a specific class. This selection process eases the difficulty of the accurate pa- rameter estimation. Thus, the important trait of the pro- posed classifier is being able to estimate simultaneously class-conditional density and inter-class boundaries to arbitrary precision. Our experimental results show that the proposed classifier not only has better performance than the conventional classifiers but also requires fewer parameters.
Year
DOI
Venue
2008
10.1109/ICPR.2008.4761788
Tampa, FL
Keywords
Field
DocType
Bayes methods,Gaussian processes,learning (artificial intelligence),pattern classification,Bayes classifier,Gaussian mixture model,class distribution estimation,incremental learning,interclass decision boundaries
Margin (machine learning),Naive Bayes classifier,Pattern recognition,Computer science,Artificial intelligence,Margin classifier,Classifier (linguistics),Statistical classification,Mixture model,Bayes classifier,Machine learning,Quadratic classifier
Conference
ISSN
ISBN
Citations 
1051-4651 E-ISBN : 978-1-4244-2175-6
978-1-4244-2175-6
2
PageRank 
References 
Authors
0.45
3
2
Name
Order
Citations
PageRank
Dwi Sianto Mansjur172.28
Biing-Hwang Juang23388699.72