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