Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width sigma. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in understanding the hyperparameter spac...

We propose a new method for constructing hyperkenels and define two promising special cases that can be computed in closed form. These we call the Gaussian and Wishart hyperkernels. The former is especially attractive in that it has an interpretable regularization scheme reminiscent of that of the Gaussian RBF kernel. We discuss how kernel learning can be used not just for improving the perform...

The purpose of this study is to investigate the accuracy of predictions of aniline removal efficiency in a moving bed biofilm reactor (MBBR) by various methods, namely by RBF, ANFIS, and fuzzy regression analysis. The reactor was operated in an aerobic batch and was filled by light expanded clay aggregate (LECA) as a carrier for the treatment of Aniline synthetic wastewater. Exploratory data an...

This paper examines the problem of categorisation of facial expressions through the use of a receptive eld neural network model, based upon novel domain Gaussian network units trained through error backpropagation. Such networks are trained upon images derived from the Ekman and Friesen \Pictures of Facial AAect" database, and they are subsequently able to successfully generalise to images of u...

Recently RBF(Radial Basis Function)-based networks have been widely used because they can learn a strong non-linear function faster and more easily by their local learning characteristics. However, it has no hidden units that can represent some global information. Accordingly even if the knowledge obtained through the previous sets of learning is utilized effectively in the present learning, th...

In this paper, a fully complex-valued radial basis function (FC-RBF) network with a fully complex-valued activation function has been proposed, and its complex-valued gradient descent learning algorithm has been developed. The fully complex activation function, sech(.) of the proposed network, satisfies all the properties needed for a complex-valued activation function and has Gaussian-like cha...

This paper presents experiments using an adaptive learning compo nent based on Radial Basis Function RBF networks to tackle the unconstrained face recognition problem using low resolution video in formation Firstly we performed preprocessing of face images to mimic the e ects of receptive eld functions found at various stages of the hu man vision system These were then used as input representat...

Corresponding Author: Fikri Budiman Department of Computer Science, University of Dian Nuswantoro, Semarang, Indonesia Email: [email protected] Abstract: Image retrieval using Support Vector Machine (SVM) classification very depends on kernel function and parameter. Kernel function used by dot product substitution from old dimension feature to new dimension depends on image dataset ...

Radial Basis Function (RBF) network based channel equalisers have a close relationship with Bayesian schemes. Decision feedback is introduced in the design of the RBF equaliser in order to reduce its computational complexity. The RBF Decision Feedback Equaliser (DFE) was found to give similar performance to the conventional mean square error (MSE) DFE over Gaussian channels using various Quadra...

In this article, we propose a simplified radial basis function (RBF) method with exterior fictitious sources for solving elliptic boundary value problems (BVPs). Three RBFs, including Gaussian, multiquadric (MQ), and inverse (IMQ) without the shape parameter, are adopted in study. With consideration of many outside domain, distance RBF is always greater than zero, such that can remove parameter...