Kevin Tee, 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.

Advisor: Laurent El Ghaoui

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