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» Entropy Numbers, Operators and Support Vector Kernels
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MLDM
2007
Springer
14 years 1 months ago
Nonlinear Feature Selection by Relevance Feature Vector Machine
Support vector machine (SVM) has received much attention in feature selection recently because of its ability to incorporate kernels to discover nonlinear dependencies between feat...
Haibin Cheng, Haifeng Chen, Guofei Jiang, Kenji Yo...
PKDD
2009
Springer
118views Data Mining» more  PKDD 2009»
14 years 2 months ago
Sparse Kernel SVMs via Cutting-Plane Training
We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. ...
Thorsten Joachims, Chun-Nam John Yu
IJON
2007
134views more  IJON 2007»
13 years 7 months ago
Analysis of SVM regression bounds for variable ranking
This paper addresses the problem of variable ranking for Support Vector Regression. The relevance criteria that we proposed are based on leave-one-out bounds and some variants and...
Alain Rakotomamonjy
JVM
2004
132views Education» more  JVM 2004»
13 years 9 months ago
Solaris Zones: Operating System Support for Server Consolidation
e a new operating system abstraction for partitioning systems, allowing multiple applications to run in isolation from each other on the same physical hardware. This isolation prev...
Andrew Tucker, David Comay
OSDI
2008
ACM
14 years 8 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