Radial Basis Function (RBF) networks are a classical family of algorithms for supervised learning. The most popular approach for training RBF networks has relied on kernel methods using regularization based on a norm in a Reproducing Kernel Hilbert Space (RKHS), which is a principled and empirically successful framework. In this paper we aim to revisit some of the older approaches to training t...

Abstract A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional networks. The current generation network is equipped with multiple kernels which provide significant performance benefits compared to previous using only a single kernel. In existing multi-kernel RBF algorithms, formed by convex combination base/...

A kernel orthogonal subspace projection (KOSP) algorithm has been developed for nonlinear approximating subpixel proportion in this paper. The algorithm applies linear regressive model to the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared-error regressor. The algorithm includes two steps: the first step is to select the feat...

<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial in hepatitis classification. These two functions were chosen due to their popularity any kernel-based machine learning method for solving classification task. The dataset then used evaluate performance of both method...

Predicting reservoir water levels helps manage droughts and floods. level is complex because it depends on factors such as climate parameters human intervention. Therefore, predicting needs robust models. Our study introduces a new model for levels. An extreme learning machine, the multi-kernel least square support vector machine (MKLSSVM), developed to predict of in Malaysia. The also novel op...

This paper describes the design of multi-category support vector machines (SVMs) for classification of bags. To train and test the SVMs a collection of 120 images of different types of bags were used (backpacks, small shoulder bags, plastic flexible bags, and small briefcases). Tests were conducted to establish the best polynomial and Gaussian RBF (radial basis function) kernels. As it is well ...

We explore ways of applying a prior on the covariance matrix of a Gaussian Process (GP) in order to increase its expressive power. We show that two well-known covariance priors, Wishart Process and Inverse Wishart Process, boil down to a two-layer feed-forward network of GPs with a particular kernel function on the neuron at the output layer. Both of these models perform supervised manifold lea...

One of the central problems in the study of Support vector machine (SVM) is kernel selection, that’s based essentially on the problem of choosing a kernel function for a particular task and dataset. By contradiction to other machine learning algorithms, SVM focuses on maximizing the generalisation ability, which depends on the empirical risk and the complexity of the machine. In the following p...

Computational units induced by convolutional kernels together with biologically inspired perceptrons belong to the most widespread types of units used in neurocomputing. Radial convolutional kernels with varying widths form RBF (radial-basis-function) networks and these kernels with fixed widths are used in the SVM (support vector machine) algorithm. We investigate suitability of various convol...