Monday March 1, 2004
DSES 6180-01 DATA MINING AND KNOWLEDGE DISCOVERY
Instructor:
Prof. Mark J. Embrechts (x 4009 or 371-4562)
Office hrs:
CII 5217 Thursday 10:00-11:00
Class Time:
Monday/Thursday 4-5:20 pm (Jonson-Rowland Science Center 2C30)
Book: Margaret H. Dunham, Data Mining: Introductory and Advanced Topics, Prentice
Hall 2003.
LECTURE #12: Direct Kernel Methods
This lecture will introduce the paradigm of direct kernel methods, which reconciles neural
networks, statistical multivariate regression and support vector machines in a single
framework. Direct kernel methods assume a kernel transform as a data preprocessing step,
rather than an inherent part of the learning method. By applying a direct kernel transform,
traditional methods such as Principal Component Analysis (PCA), Ridge Regression, Partial
Least Squares (PLS), Independent Component Analysis (ICA) or simple one-layered neural
networks, can be transformed into powerful nonlinear modeling and machine learning tools.
This presentation will highlight several industrial applications of direct kernel methods such
as network intrusion detection, the detection of ischemia from magnetocariograms, in-silico
drug design, the electronic nose, gene expression arrays, and the detection of mixtures of chemical
substances from spectral data.
Handouts
1. Lecture Slides posted on website
2. Mark J. Embrechts, “Direct Kernel Least-Squares Support Vector Machines with Heuristic
Regularization,” Submitted for presentation to IJCNN2004, Budapest, July 2004.
Quiz
1. Explain PLS in half a page or less
2. Explain equation 8
Deadlines:
January 22
January 29
February 16
March 1
March 4
March 18
March 8&11
March 15
April 8
April 22/26
HW#0 (Web browsing).
Project Proposal
HW #1
Quiz #1 on PLS paper by Svante Wold et al.
HW #2
HW #3
Spring Break
Progress Report
No Class
Final Presentations
1