◎    課程時間：98年6月29日 下午2:00~5:30

                           98年6月30日  上午9:00~2:00

◎    課程地點：臺灣大學博理館201會議室

◎    主講人：貢三元 教授

◎    課程內容：

      1. Kernel-Based Unsupervised/supervised Machine Learning: Overview

      2. Kernel Based Representation Spaces

          •  2.1 Basic Kernel Hilbert Space

          •  2.2 Spectral Space

          •  2.3 Empirical Space

          •  2.4 Linear Mapping Betw. Spaces

     3. Principal Component analysis (Kernel PCA)

     4. Supervised Kernel Classifiers

          •  4.1 Fisher Discriminant Analysis (FDA)

          •  4.2 Kernel Fisher Discriminant (KFD)

          •  4.3 Kernel-based  Support Vector Machine (SVM)

          •  4.4 Fisher-SVM Hybrid Classifier

          •  4.5 Regularization:  Robustification of Training

      5. Clustering Methods for PD Kernels 

          •  5.1 K-means and Convergence

          •  5.2 Kernel K-means  

          •  5.3 Spectral Space: Dimension-Reduction 

          •  5.4 Kernel NJ: Hierarchical Clustering

          •  5.5 Fast Kernel Trick

      6. Extension to Nonvectorial Data and NPD (None-Positive-definite) Kernels.