A statistical learning framework for data mining of large-scale systems : algorithms, implementation, and applications

A machine learning framework is presented that supports data mining and statistical modeling of systems that are monitored by large-scale sensor networks. The proposed algorithm is novel in that it takes both observations and domain knowledge into consideration and provides a mechanism that combines analytical modeling and inductive learning. An efficient solver is presented that allow the algorithm to solve large-scale problems efficiently. The solver uses a randomized kernel that incorporates domain knowledge into support vector machine learning. It also takes advantage of the sparseness of support vectors and this allows for parallelization and online training to further speed-up of the computation. The solver can be integrated into existing systems, embedded into databases, or exposed as a web service. Understanding the data generated by large-scale system presents several problems. First, statistical modeling approaches may either under-fit or over-fit the data and are sensitive to data quality. Second, learning is a computational extensive process and often becomes intractable when the sample size exceeds several thousands.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Tsou, Ching-Huei, 1973-

Advisor dc:contributor.advisor

• John R. Williams.

• Civil and Environmental Engineering.

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/1721.1/38578
• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Identifier URI

http://dspace.mit.edu/handle/1721.1/38578

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/38578