課程名稱：Kernel Techniques for Supervised/Unsupervised Machine Learning
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