Monday March 1, 2004
DSES-4810-01 Intro to COMPUTATIONAL INTELLIGENCE
& SOFT COMPUTING
Instructor:
Office Hours:
Class Time:
TEXT:
Prof. Mark J. Embrechts (x 4009 or 371-4562) (embrem@rpi.edu)
Thursday 10-11 am (CII5217), or by appointment.
Monday/Thursday: 8:30-9:50 (Amos eaton 216)
J. S. Jang, C. T. Sun, E. Mizutani, “Neuro-Fuzzy and Soft Computing,”
Prentice Hall, 1996. (1998) ISBN 0-13-261066-3
LECTURES #12&13: 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.
Deadlines
January 22
January 29
February 17
February 19
February 23
March 16
April 8
April 22/April 26
Homework Problem #0 (web browsing)
Project Proposal
Homework Problem #1 (paper)
Quiz #1 (20 minutes, Chapter 2 of Book)
Homework Problem #2 (peaks function)
Progress Report
No Class
Final Presentations
1