Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers.

DOI10.1016/S0031-3203(03)00136-5zbMath1059.68101OpenAlexW2171428093MaRDI QIDQ1426206

Nicola L. C. Talbot, Gavin C. Cawley

Publication date: 14 March 2004

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0031-3203(03)00136-5

zbMATH Keywords

Model selectionCross-validationKernel Fisher discriminant analysis

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items

Modified characteristics projection finite element method for time-dependent conduction-convection problems, Optimally regularised kernel Fisher discriminant classification, Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression, Pseudo-inverse linear discriminants for the improvement of overall classification accuracies, Advances in credit scoring: combining performance and interpretation in kernel discriminant analysis, Integrated Fisher linear discriminants: an empirical study, The leave-worst-\(k\)-out criterion for cross validation, On the Choice of the Kernel Function in Kernel Discriminant Analysis Using Information Complexity, A reformative kernel Fisher discriminant analysis, Additive regularization trade-off: fusion of training and validation levels in kernel methods, Sparse multinomial kernel discriminant analysis (sMKDA), A fast kernel-based nonlinear discriminant analysis for multi-class problems, Efficient approximate leave-one-out cross-validation for kernel logistic regression, Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation, HYPER-PARAMETER SELECTION FOR SPARSE LS-SVM VIA MINIMIZATION OF ITS LOCALIZED GENERALIZATION ERROR, Kernel learning at the first level of inference, Hierarchical clustered multiclass discriminant analysis via cross-validation

Uses Software

• alr3

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Support-vector networks
• Some results on Tchebycheffian spline functions and stochastic processes
• Updating the Inverse of a Matrix
• The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
• Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix
• Theory of Reproducing Kernels
• An Inverse Matrix Adjustment Arising in Discriminant Analysis
• Soft margins for AdaBoost
• Choosing multiple parameters for support vector machines