Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers

By Gavin C. Cawley, Nicola L. C. Talbot. Pattern Recognition, 2003

440 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d328d2f9f63ee9f813a9653a5d11bbdff018dac4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

An exploration strategy for non-stationary opponents (2017, methodology): “Also, computing leave-one-out cross validation (extreme case of k-fold cross validation) is rarely adopted in large-scale applications because it is computationally expensive [10].”
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters (2007, positive): “, 2002), Gaussian Process (GP) (Rasmussen and Williams, 2006; Sundararajan and Keerthi, 2001), Least-Squares Support Vector Machine (LS-SVM) (Suykens and Vandewalle, 1999; Cawley and Talbot, 2004), Kernel Fisher Discriminant (KFD) analysis (Mika et al., 1999; Cawley and Talbot, 2003; Saadi et al., 2”
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation (2010, methodology): “An attractive feature of kernel ridge regression is that it is possible to perform leave-one-out cross-validation in closed form, with minimal cost as a by-product of the training algorithm (Cawley and Talbot, 2003).”
Towards transferring skills to flexible surgical robots with programming by demonstration and reinforcement learning (2016, background): “Many approaches are able to solve the linear regression problem, like gradient descent [22] and normal equation [23].”
KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition (2005, methodology): “Subsequent research saw the development of a series of KFD algorithms (see Baudat and Anouar [6], Roth and Steinhage [7], Mika et al. [8], [9], [10], Yang [11], Lu et al. [12], Xu et al. [13], Billings and Lee [14], Gestel et al. [15], Cawley and Talbot [16], and Lawrence and Schö lkopf [17]).”
Applied MANOVA and discriminant analysis (2006, background): “Cawley and Talbot (2003) propose a two-group L-O-O analysis for the kernel function approach of linear PDA.”