Yaohui Ye and Wenxuan Cai and Benjamin Le and Jefferson Lai and Pierce Vollucci

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2015-112

May 14, 2015

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-112.pdf

This capstone project report covers the research and development of Smart Anomaly Detection and Subscriber Analysis in the domain of Online Video Data Analytics. In the co-written portions of this document, we discuss the projected commercialization success of our products by analyzing worldwide trends in online video, presenting a competitive business strategy, and describing several approaches towards the management of our intellectual property. In the individually written portion of this document, we discuss our implementation of two machine learning techniques, k-means clustering and logistic regression, and give detailed evaluation of these techniques on our dataset.

Advisors: George Necula

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BibTeX citation:

@mastersthesis{Ye:EECS-2015-112,
    Author= {Ye, Yaohui and Cai, Wenxuan and Le, Benjamin and Lai, Jefferson and Vollucci, Pierce},
    Editor= {Necula, George and Wroblewski, Don},
    Title= {Online Video Data Analytics},
    School= {EECS Department, University of California, Berkeley},
    Year= {2015},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-112.html},
    Number= {UCB/EECS-2015-112},
    Abstract= {This capstone project report covers the research and development of Smart Anomaly Detection and Subscriber Analysis in the domain of Online Video Data Analytics. In the co-written portions of this document, we discuss the projected commercialization success of our products by analyzing worldwide trends in online video, presenting a competitive business strategy, and describing several approaches towards the management of our intellectual property. In the individually written portion of this document, we discuss our implementation of two machine learning techniques, k-means clustering and logistic regression, and give detailed evaluation of these techniques on our dataset.},
}

EndNote citation:

%0 Thesis
%A Ye, Yaohui 
%A Cai, Wenxuan 
%A Le, Benjamin 
%A Lai, Jefferson 
%A Vollucci, Pierce 
%E Necula, George 
%E Wroblewski, Don 
%T Online Video Data Analytics
%I EECS Department, University of California, Berkeley
%D 2015
%8 May 14
%@ UCB/EECS-2015-112
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-112.html
%F Ye:EECS-2015-112