Kernel optimization using nonparametric Fisher criterion in the subspace
Authors:
- 1 March 2015
- journal article
- research article
- Published by Elsevier in Pattern Recognition Letters
- Vol. 54, 43-49
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61103097)
This publication has 17 references indexed in Scilit:
- Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiersPattern Recognition Letters, 2014
- Learning kernel parameters for kernel Fisher discriminant analysisPattern Recognition Letters, 2013
- Power failure: why small sample size undermines the reliability of neuroscienceNature Reviews Neuroscience, 2013
- On the equivalence of Kernel Fisher discriminant analysis and Kernel Quadratic Programming Feature SelectionPattern Recognition Letters, 2011
- Kernel Optimization in Discriminant AnalysisIEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
- Gaussian kernel optimization for pattern classificationPattern Recognition, 2009
- A Kernel-Induced Space Selection Approach to Model Selection in KLDAIEEE Transactions on Neural Networks, 2008
- Feature Selection with Kernel Class SeparabilityIEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
- Choosing Parameters of Kernel Subspace LDA for Recognition of Face Images Under Pose and Illumination VariationsIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2007
- Choosing Multiple Parameters for Support Vector MachinesMachine Learning, 2002