Course  Reading and Discussion Lists

• Tutorial
  • A. Jain, R. Duin and J. Mao, "Statistical Pattern Recognition: A Review", IEEE T-PAMI, 22:4-37, 2000.
    
    
• ICA
• A. Hyvarinen and E. Oja, "Independent Component Analysis: Algorithms and Applications", Neural Networks, 13(4-5):411-430, 2000.
   
• Nearest Neighbor Classification
• K. Weinberger, J. Blitzer and L. Saul, "Distance Metric Learning for Large Margin Nearest Neighbor Classification", Neural Information Processing Systems, 2006.
  
  
• Spectral Clustering
• Jianbo Shi and Jitendra Malik, "Normalized Cuts and Image Segmentation", CVPR'97

 
• Mean Shift 
• D. Comaniciu and P. Meer, "Mean Shift: A Robust Approach Toward Feature Space Analysis", IEEE T-PAMI, 24(5):603-619, 2002.

 
• Support Vector Machines and Kernel Machines
• K. Sung and T. Poggio, "Example-based Learning for View-based Human Face Detection", IEEE T-PAMI, 1998
• S. Dumais, E. Osuna, J. Platt and B. Scholkopf, "Support Vector Machine", IEEE Intelligent Systems, July, 1998
• Tutorail I: by C. Burges, 1998
• Totorial II: by A. Smola and B. Scholkopf, 1998


1.  
• Dimension reduction and embedding
• J. Tenenbaum, V. de Silva, and J. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction", Science, 290:2319-2323, December 2000.
• S. Roweis and L. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding", Science, 290:2323-2326, December, 2000.
• M. Steyvers, "Multidimensional Scaling", Encyclopedia of Cognitive Science, 2002.
  
  
• Boosting
  • R. Schapire, "The Boosting Approach to Machine Learning: An Overview", Nonlinear Estimation and Classification, Springer, 2003.
  • Y. Freund and R. Schapire, "Experiments with a New Boosting Algorithm", Proc. Int'l Conf. on Machine Learning, 1996.
    
    
• Hidden Markov Models
• Rabiner's tutorial, 1989
  
  
• Random Fields
  • W. Freeman and E. Pasztor, "Learning Low-level Vision", Proc. IEEE Int'l Conf. on Computer Vision, 1999.
  • J. Lafferty, A. McCallum and F. Pereira, "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data", Proc. Int'l Conf. on Machine Learning, 2001.