CS 607: Topics in Computer Science
Course Syllabus -- Spring 1998

Professor: C. Tamon
Office: 369 Science Center
Phone: 268-6521
E-mail: tino@sun.mcs.clarkson.edu

Office Hours: M,W,F 10:00-12:00 and by appointment

Bagging, Boosting, and Fourier Transform

Purpose: The goal of this project is to investigate several open problems in machine learning that involve bootstrap aggregating (bagging), boosting, and Fourier transform methods.

Background: Bagging is a well-known method in statistics to improve the performance of predictors (see Efron & Tibshirani (1993), Breiman (1996)). Boosting is a similar method that was developed in learning theory (see Freund & Schapire (1995)) and is somewhat superior than bagging because of its adaptive properties. Unfortunately, boosting is inherently sequential in nature whereas bagging admits a relatively simple parallel implementation. The question is whether there is a parallel compromise between these two methods.

A powerful theoretical learning algorithm based on Fourier transform and boosting was developed by Jackson (1994) that can learn expressive Boolean representation classes. Although the algorithms runs in polynomial time, in practice, the time bound is still too high to be of any use. We explore alternatives for improving Jackson's algorithm from both the Fourier and the boosting aspects.

The following specific quesitons are currently being investigated:

  1. Parallel implementations of boosting
  2. Using approximation algorithms for MAXCUT to improve boosting algorithms
  3. Efficiency improvements on a boosting-based Fourier transform learning algorithm
Participants: Anthony Moores, David Stracuzzi and Christino Tamon.

Last modified: 20 January 1998