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中興電機20090609和20090611短期課程

更新時間:2009-06-03 14:20:04 / 張貼時間:2009-06-03 14:20:04
電機系林志鴻
單位電機系
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課程名稱:Kernel Techniques for Supervised/Unsupervised Machine Learning

授課教師:貢三元客座講座教授(中興大學電機系)    

時間:6月9日(星期二)6月11日(星期四) 下午1:00~3:30

地點:電機大樓507會議室

Abstract:
Machine learning techniques are useful for data mining and pattern recognition when only pairwise relationships of the training objects are known - as opposed to the training objects themselves. Such pairwise learning approach has a special appeal to many practical applications such as multimedia and bioinformatics, where a variety of heterogeneous sources of information are available.  The primary information used in the kernel approach  is either (1) the kernel matrix (K) associated with either  vectorial data or (2) the similarity matrix (S) associated with  nonvectorial objects.

The short course will cover the following topics:
1. Kernel-Based   Unsupervised/supervised Machine Learning:  Overview
2. Kernel Principal Component Analysis (Kernel PCA)
3. Kernel Fisher Discriminant (KFD) analysis.
4. Kernel-based  Support Vector Machine and Robustification of Training.
5. Fast Clustering Methods  (Kernel Component Analysis and Kernel Trick)
6. Extension to Nonvectorial Data and NPD (None-Positive-definite) Kernels

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