2026-09-18T20:28:55Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/412592022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Una-May O'Reilly.
Chen, Kathy F. (Kathy Fang-Yun)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-04-23T14:36:28Z
2008-04-23T14:36:28Z
2007
2007
http://hdl.handle.net/1721.1/41259
213413306
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 51-52).
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.
by Kathy F. Chen.
M.Eng.
52 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
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permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Offline and online SVM performance analysis
Offline and online Support Vector Machine performance analysis
Thesis
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