Distributed Support Vector Machine Learning

Support Vector Machines (SVMs) are used for a growing number of applications. A fundamental constraint on SVM learning is the management of the training set. This is because the order of computations goes as the square of the size of the training set. Typically, training sets of 1000 (500 positives and 500 negatives, for example) can be managed on a PC without hard-drive thrashing. Training sets of 10,000 however, simply cannot be managed with PC-based resources. For this reason most SVM implementations must contend with some kind of chunking process to train parts of the data at a time (10 chunks of 1000, for example, to learn the 10,000). Sequential and multi-threaded chunking methods provide a way to run the SVM on large datasets while retaining accuracy. The multi-threaded distributed SVM described in this thesis is implemented using Java RMI, and has been developed to run on a network of multi-core/multi-processor computers.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2008

Author dc:creator

• Armond, Kenneth C., Jr.

Contributors dc:contributor

• Winters-Hilt, Stephen
• Summa, Christopher
• Zhu, Dongxiao

• Distributed
• Parallel
• SVM
• Support Vector Machine
• Machine Learning
• SMO
• Sequential Minimization Optimization

Repository record dc:identifier

https://scholarworks.uno.edu/td/711

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1711