Offline and online SVM performance analysis

To understand and evaluate the performance of a machine learning algorithm, the Support Vector Machine, this thesis compares the strengths and weaknesses between the offline and online SVM. The work includes the performance comparisons of SVMLight and LaSVM, with results of training time, number of support vectors, kernel evaluations, and test accuracies. Multiple datasets are experimented to cover a wide range of input data and training problems. Overall, the online LaSVM has trained with less time and returned comparable test accuracies than SVMLight. A general breakdown of the two algorithms and their computation efforts are included for detailed analysis.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Chen, Kathy F. (Kathy Fang-Yun)

Advisor dc:contributor.advisor

• Una-May O'Reilly.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/41259

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/41259