This paper proposes a new algorithm for training support vector machines: Sequential
Minimal Optimization, or SMO. Training a support vector machine requires the solution of
a very large quadratic programming (QP) optimization problem. SMO breaks this large
QP problem into a series of smallest possible QP problems. These small QP problems are
solved analytically, which avoids using a time-consuming numerical QP optimization as an
inner loop. The amount of memory required for SMO is linear in...
%0 Generic
%1 platt1998sequential
%A Platt, J.
%D 1998
%K lecture:2018 lecture:data-mining optimization smo svm
%T Sequential minimal optimization: A fast algorithm for training support vector machines
%U http://citeseer.ist.psu.edu/platt98sequential.html
%X This paper proposes a new algorithm for training support vector machines: Sequential
Minimal Optimization, or SMO. Training a support vector machine requires the solution of
a very large quadratic programming (QP) optimization problem. SMO breaks this large
QP problem into a series of smallest possible QP problems. These small QP problems are
solved analytically, which avoids using a time-consuming numerical QP optimization as an
inner loop. The amount of memory required for SMO is linear in...
@misc{platt1998sequential,
abstract = {This paper proposes a new algorithm for training support vector machines: Sequential
Minimal Optimization, or SMO. Training a support vector machine requires the solution of
a very large quadratic programming (QP) optimization problem. SMO breaks this large
QP problem into a series of smallest possible QP problems. These small QP problems are
solved analytically, which avoids using a time-consuming numerical QP optimization as an
inner loop. The amount of memory required for SMO is linear in...},
added-at = {2018-06-18T00:51:26.000+0200},
author = {Platt, J.},
biburl = {https://www.bibsonomy.org/bibtex/2083999b789ad60c36ea734068008dd28/nosebrain},
citeulike-article-id = {678621},
description = {citeulike},
interhash = {30b12f2974d9c9f94476c16de1aa3688},
intrahash = {083999b789ad60c36ea734068008dd28},
keywords = {lecture:2018 lecture:data-mining optimization smo svm},
priority = {2},
timestamp = {2018-06-18T00:52:56.000+0200},
title = {Sequential minimal optimization: A fast algorithm for training support vector machines},
url = {http://citeseer.ist.psu.edu/platt98sequential.html},
year = 1998
}