17 search results - page 1 / 4 » Maximum-Gain Working Set Selection for SVMs |
JMLR
2006
13 years 4 months ago
2006
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
ESANN
2008
13 years 6 months ago
2008
Sequential Minimal Optimization (SMO) is currently the most popular algorithm to solve large quadratic programs for Support Vector Machine (SVM) training. For many variants of this...
JMLR
2011
12 years 11 months ago
2011
We develop, analyze, and test a training algorithm for support vector machine classifiers without offset. Key features of this algorithm are a new, statistically motivated stoppi...
PAKDD
2007
ACM
13 years 10 months ago
2007
ACM
Recent work shows that Support vector machines (SVMs) can be solved efficiently in the primal. This paper follows this line of research and shows how to build sparse support vector...
ESANN
2007
13 years 6 months ago
2007
One-class support vector machines (1-SVMs) estimate the level set of the underlying density observed data. Aside the kernel selection issue, one difficulty concerns the choice of t...