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| Paper: | MLSP-L1.3 |
| Session: | Pattern Recognition and Classification I |
| Time: | Thursday, May 20, 10:10 - 10:30 |
| Presentation: |
Lecture |
| Topic: |
Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification |
| Title: |
LEARNING BASED ON KERNEL DISCRIMINANT-EM ALGORITHM FOR IMAGE CLASSIFICATION |
| Authors: |
Qi Tian; University of Texas, San Antonio | | |
| | Jerry (Jie) Yu; University of Texas, San Antonio | | |
| | Ying Wu; Northwestern University | | |
| | Thomas S. Huang; University of Illinois at Urbana-Champaign | | |
| Abstract: |
In image classification and other learning-based object recognition tasks, it is often tedious and expensive to label large training data sets. Discriminant-EM (DEM) proposed a semi-supervised learning framework which takes both labeled and unlabeled data to learn classifiers. This paper extends the linear D-EM to nonlinear kernel algorithm, KDEM and evaluates KDEM systematically on both benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated. |
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