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» Efficient implementation of SVM for large class problems
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OSDI
2008
ACM
14 years 7 months ago
Predicting Computer System Failures Using Support Vector Machines
Mitigating the impact of computer failure is possible if accurate failure predictions are provided. Resources, applications, and services can be scheduled around predicted failure...
Errin W. Fulp, Glenn A. Fink, Jereme N. Haack
UAI
2001
13 years 8 months ago
A Clustering Approach to Solving Large Stochastic Matching Problems
In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The...
Milos Hauskrecht, Eli Upfal
ICASSP
2009
IEEE
13 years 5 months ago
Target detection using incremental learning on single-trial evoked response
The human neural responses associated with cognitive events, referred as event related potentials (ERPs), can provide reliable inference for target image detection. Incremental le...
Yonghong Huang, Deniz Erdogmus, Misha Pavel, Kenne...
GECCO
2007
Springer
212views Optimization» more  GECCO 2007»
13 years 11 months ago
Controlling overfitting with multi-objective support vector machines
Recently, evolutionary computation has been successfully integrated into statistical learning methods. A Support Vector Machine (SVM) using evolution strategies for its optimizati...
Ingo Mierswa
JMLR
2006
150views more  JMLR 2006»
13 years 7 months ago
Building Support Vector Machines with Reduced Classifier Complexity
Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overc...
S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCos...