Name
Title | ||
|---|---|---|
Improving Kernel Density Classifier Using Corrective Bandwidth Learning with Smooth Error Loss Function |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICMLA.2008.49 | ICMLA |
Keywords | DocType | Citations |
large data size,empirical mixture model,smooth error loss,improving kernel density classifier,proposed bandwidth,proposed classifier,corrective bandwidth learning,conventional kde-based classifier,various data size,expected error-rate,additional experiment,corrective bandwidth,kernel density estimation,bandwidth,gradient descent,error rate,mixture model,artificial neural networks,multi class classification,training data,kernel density estimate,loss function,learning artificial intelligence,classification algorithms,nickel,machine learning,kernel,kernel density | Conference | 1 |
PageRank | References | Authors |
0.37 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dwi Sianto Mansjur | 1 | 7 | 2.28 |
| Biing-Hwang Juang | 2 | 3388 | 699.72 |