The problem of detecting “atypical objects” or “outliers” is one of the classical topics in (robust) statistics. Recently, it has been proposed to address this problem by means of one-class SVM classifiers. The main conceptual shortcoming of most one-class approaches, however, is that in a strict sense they are unable to detect outliers, since the expected fraction of outliers has to be specifi...

In this paper, we present a novel technique for restoring a blurred noisy image without any prior knowledge of the blurring function and the statistics of noise. The technique combines wavelet transform with radial basis function (RBF) neural network to restore the given image which is degraded by Gaussian blur and additive noise. In the proposed technique, the wavelet transform is adopted to d...

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on ...

Recently inverse halftoning techniques are applied in many image processing applications, in which an efficient inverse halftoning method, that provides high quality gray-scale image from any binary halftone image, is required. In this paper we propose two neural networks based inverse halftoning methods, which are Multilayer Perceptron (MLP)-based and Radial Basis Function (RBF)-based inverse ...

In this paper, a new kernel function is introduced that improves the classification accuracy of support vector machines (SVMs) for both linear and non-linear data sets. The proposed kernel function, called Gauss radial basis polynomial function (RBPF) combine both Gauss radial basis function (RBF) and polynomial (POLY) kernels. It is shown that the proposed kernel converges faster than the RBF ...

A numerical method is proposed to approximate the inverse of a general bi-Lipschitz nonlinear dimensionality reduction mapping, where the forward and consequently the inverse mappings are only explicitly defined on a discrete dataset. A radial basis function (RBF) interpolant is used to independently interpolate each component of the high-dimensional representation of the data as a function of ...

Among the many methods proposed in the literature for face recognition, those relying on the so called eigenfaces have been explored with great interest in the last few years. In those methods the face images are initially subjected to a PCA stage (Principal Component Analysis) for dimensionality reduction and then applied to a classifier. This work evaluates and compares two eigenface based fa...

In this paper we present an integer programming formulation of the minimum sphere covering problem that seeks to construct a minimum number of spheres to represent the training data. Using soft threshold functions, we further derive a linear programming problem whose solution gives rise to radial basis function classifiers and sigmoid function classifiers. In contrast to traditional RBF and sig...

In this paper a Glial Ratio (g-ratio) mix hybrids of 67% Sigmoid and 33% Radial functions (HSCR-BFgr) based on Particle swarm optimisation with the highest survivability of all possible routing redundancies, reliability, efficiency, fault tolerant with minimum fitness error is proposed for underground rescue operation. Nonlinear weights of cosine and sine were imposed on the g-ratio hybrids. In...

The Gaussian radial basis function (RBF) is a widely used kernel function in support vector machine (SVM). The kernel parameter σ is crucial to maintain high performance of the Gaussian SVM. Most previous studies on this topic are based on optimization search algorithms that result in large computation load. In this paper, we propose an analytical algorithm to determine the optimal σ with the p...