Estimating the Number of Components in a Mixture of Multilayer Perceptrons

BIC criterion is widely used by the neural-network community for model selection tasks, although its convergence properties are not always theoretically established. In this paper we will focus on estimating the number of components in a mixture of multilayer perceptrons and proving the convergence of the BIC criterion in this frame. The penalized marginal-likelihood for mixture models and hidden Markov models introduced by Keribin (2000) and, respectively, Gassiat (2002) is extended to mixtures of multilayer perceptrons for which a penalized-likelihood criterion is proposed. We prove its convergence under some hypothesis which involve essentially the bracketing entropy of the generalized score-functions class and illustrate it by some numerical examples

Texture classification of images from endoscopic capsule by using MLP and SVM – a comparative approach

This article reports a comparative study of Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) in the classification of endoscopic capsule images. Texture information is coded by second order statistics of color image levels extracted from co-occurrence matrices. The co-occurrence matrices are computed from images rich in texture information. These images are obtained by processing the original images in the wavelet domain in order to select the most important information concerning texture description. Texture descriptors calculated from co-occurrence matrices are then modeled by using third and forth order moments in order to cope with non-Gaussianity, which appears especially in some pathological cases. Several color spaces are used, namely the most simple RGB, the most related to the human perception HSV, and the one that best separates light and color information, which uses luminance and color differences, usually known as YCbCr.Centre Algoritm

Improved streamflow forecasting using self-organizing radial basis function artificial neural networks

Streamflow forecasting has always been a challenging task for water resources engineers and managers and a major component of water resources system control. In this study, we explore the applicability of a Self Organizing Radial Basis (SORB) function to one-step ahead forecasting of daily streamflow. SORB uses a Gaussian Radial Basis Function architecture in conjunction with the Self-Organizing Feature Map (SOFM) used in data classification. SORB outperforms the two other ANN algorithms, the well known Multi-layer Feedforward Network (MFN) and Self-Organizing Linear Output map (SOLO) neural network for simulation of daily streamflow in the semi-arid Salt River basin. The applicability of the linear regression model was also investigated and concluded that the regression model is not reliable for this study. To generalize the model and derive a robust parameter set, cross-validation is applied and its outcome is compared with the split sample test. Cross-validation justifies the validity of the nonlinear relationship set up between input and output data. © 2004 Elsevier B.V. All rights reserved