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ML
2002
ACM
107views Machine Learning» more  ML 2002»
13 years 7 months ago
Training Invariant Support Vector Machines
Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated...
Dennis DeCoste, Bernhard Schölkopf
VLSID
2005
IEEE
105views VLSI» more  VLSID 2005»
14 years 1 months ago
Placement and Routing for 3D-FPGAs Using Reinforcement Learning and Support Vector Machines
The primary advantage of using 3D-FPGA over 2D-FPGA is that the vertical stacking of active layers reduce the Manhattan distance between the components in 3D-FPGA than when placed...
R. Manimegalai, E. Siva Soumya, V. Muralidharan, B...
ICML
2005
IEEE
14 years 8 months ago
Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning
We introduce a novel approach to incorporating domain knowledge into Support Vector Machines to improve their example efficiency. Domain knowledge is used in an Explanation Based ...
Qiang Sun, Gerald DeJong
JSS
2008
317views more  JSS 2008»
13 years 7 months ago
Predicting defect-prone software modules using support vector machines
Effective prediction of defectprone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Supp...
Karim O. Elish, Mahmoud O. Elish
ESANN
2000
13 years 9 months ago
Algorithmic approaches to training Support Vector Machines: a survey
: Support Vector Machines (SVMs) have become an increasingly popular tool for machine learning tasks involving classi cation, regression or novelty detection. They exhibit good gen...
Colin Campbell