Project Title: Data Mining Using Support Vector Machines

Adviser: Dr. Hickman

Description: Support vector machines (SVM) have become increasingly popular tools for data mining tasks such as data classification. A SVM uses optimization algorithms to determine the appropriate classification based on characteristics of the input. Specifically, the input data is mapped to another vector space and the SVM is designed to identify linear relations in the new space. Linear programming is used iteratively to separate the data into various classes.

Project requirements:

Study and understand the paper: Bennett & Campbell, 2000, Support Vector Machines: Hype or Hallelujah?, SIGKDD Explorations 2:2, pp 1-13.

Study additional resources including:

Cristianini & Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 2000, Cambridge University Press.

Schkopf & Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2001, MIT Press.

Implement a SVM in Maple (or similar language) and test your SVM code on standard data classification problems from existing test suites (computational emphasis). Or adapt and apply existing public domain SVM software to solve classification problems in a new domain such as medicine, criminal justice, or remote sensing (application emphasis).

Prepare a report to presented at the UNO Math Department MAM Symposium in April 2005.

Students interested in this project should have completed or be currently enrolled in MATH 4300 and be comfortable working in a computational environment.