Kevin Tee and Xinchen Ye and Weijia Jin

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2015-129

May 15, 2015

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-129.pdf

We take an algorithmic and computational approach to the problem of providing patent recommendations, developing a web interface that allows users to upload their draft patent and returns a list of ranked relevant patents in real time. We develop scalable, distributed algorithms based on optimization techniques and sparse machine learning, with a focus on both accuracy and speed.

Advisors: Laurent El Ghaoui

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BibTeX citation:

@mastersthesis{Tee:EECS-2015-129,
    Author= {Tee, Kevin and Ye, Xinchen and Jin, Weijia},
    Title= {Large Scale Text Analysis},
    School= {EECS Department, University of California, Berkeley},
    Year= {2015},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-129.html},
    Number= {UCB/EECS-2015-129},
    Abstract= {We take an algorithmic and computational approach to the problem of providing patent recommendations, developing a web interface that allows users to upload their draft patent and returns a list of ranked relevant patents in real time. We develop scalable, distributed algorithms based on optimization techniques and sparse machine learning, with a focus on both accuracy and speed.},
}

EndNote citation:

%0 Thesis
%A Tee, Kevin 
%A Ye, Xinchen 
%A Jin, Weijia 
%T Large Scale Text Analysis
%I EECS Department, University of California, Berkeley
%D 2015
%8 May 15
%@ UCB/EECS-2015-129
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-129.html
%F Tee:EECS-2015-129