Kernel-Based Nonlinear Beamforming Construction Using Orthogonal Forward Selection With the Fisher Ratio Class Separability Measure

Kernel Density Construction Using Orthogonal Forward Regression

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construct...

Learning Kernel Parameters by using Class Separability Measure

Learning kernel parameters is important for kernel based methods because these parameters have significant impact on the generalization abilities of these methods. Besides the methods of Cross-Validation and Leave-One-Out, minimizing some upper bounds on the generalization error, such as the radius-margin bound, was also proposed to more efficiently learn the optimal kernel parameters. In this ...

Fast Kernel Classifier Construction Using Orthogonal Forward Selection to Minimise Leave-One-Out Misclassification Rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier’s generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very effi...

Feature Selection with Kernel Class Separability (Appendix Only)

Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model

A sparse regression modelling technique is developed using a generalised kernel model in which each kernel regressor has its individually tuned position (centre) vector and diagonal covariance matrix. An orthogonal least squares forward selection procedure is employed to append the regressors one by one. After the determination of the model structure, namely the selection of an appropriate numb...