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» Optimizing F-Measure with Support Vector Machines
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NIPS
2004
13 years 9 months ago
Parallel Support Vector Machines: The Cascade SVM
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. I...
Hans Peter Graf, Eric Cosatto, Léon Bottou,...
PVM
2005
Springer
14 years 1 months ago
Some Improvements to a Parallel Decomposition Technique for Training Support Vector Machines
We consider a parallel decomposition technique for solving the large quadratic programs arising in training the learning methodology Support Vector Machine. At each iteration of th...
Thomas Serafini, Luca Zanni, Gaetano Zanghirati
IFIP7
2001
Springer
137views Optimization» more  IFIP7 2001»
13 years 12 months ago
Data Mining via Support Vector Machines
Support vector machines (SVMs) have played a key role in broad classes of problems arising in various fields. Much more recently, SVMs have become the tool of choice for problems...
Olvi L. Mangasarian
ICONIP
2004
13 years 9 months ago
Content-Based Video Classification Using Support Vector Machines
Abstract. In this paper, we investigate the problem of video classification into predefined genre. The approach adopted is based on spatial and temporal descriptors derived from sh...
Vakkalanka Suresh, C. Krishna Mohan, R. Kumara Swa...
ICPR
2008
IEEE
14 years 1 months ago
Fast model selection for MaxMinOver-based training of support vector machines
OneClassMaxMinOver (OMMO) is a simple incremental algorithm for one-class support vector classification. We propose several enhancements and heuristics for improving model select...
Fabian Timm, Sascha Klement, Thomas Martinetz