This paper presents a new approach to model selection based on hypothesis testing. We 4rst describe a procedure to generate di5erent scores for any candidate model from a single sample of training data and then discuss how to apply multiple comparison procedures (MCP) to model selection. MCP statistical tests allow us to compare three or more groups of data while controlling the probability of ...

Scatterograms of images of training vectors in the hidden space help to evaluate the quality of neural network mappings and understand internal representations created by the hidden layers. Visualization of these representations leads to interesting conclusions about optimal architectures and training of such networks. The usefulness of visualization techniques is illustrated on parity problems...

In this paper, we investigate the decision making ability of a fully complex-valued radial basis function (FC-RBF) network in solving real-valued classification problems. The FC-RBF classifier is a single hidden layer fully complex-valued neural network with a nonlinear input layer, a nonlinear hidden layer, and a linear output layer. The neurons in the input layer of the classifier employ the ...

ÐThis is an elementary research for assigning color values to voxels of multichannel Magnetic Resonance Imaging (MRI) volume data. The MRI volume data sets obtained under different scanning conditions are transformed to the components by independent component analysis (ICA), which enhances physical characteristics of the tissue. The transfer functions for generating color values from independen...

Partial differential equations (PDEs) with Dirichlet boundary conditions defined on boundaries with simple geomerty have been succesfuly treated using sigmoidal multilayer perceptrons in previous works [1, 2]. This article deals with the case of complex boundary geometry, where the boundary is determined by a number of points that belong to it and are closely located, so as to offer a reasonabl...

In this paper it is proposed an image compression method based on the idea of fitting a set of Neural Networks (NNs) outputs to the image surface, which is a three-dimensional surface where the pixel values are considered as heights (z-values) defined on the x–y ground plane. An image is divided into subimages (blocks) using a quad tree, according to the complexity of the image surface. Individ...

Neural networks are powerful computational tools, and have been applied in various applications. In this work, a neural network has been used to solve a pattern classification problem encountered in biochemistry. One of the major topics of research in molecular biology is the prediction of functional properties of biomacromolecules from their sequence data. A radial basis function (RBF) network...

Finance and investing is the second most frequent business area of neural networks applications after production/operations. Although many research results show that neural networks can solve almost all problems more efficiently than traditional modeling and statistical methods, there are opposite research results showing that statistical methods in particular data samples outperform neural net...

Even though multilayer perceptrons and radial basis function networks belong to the class of artificial neural networks and they are used for similar tasks, they have very different structures and training mechanisms. So, some researchers showed better performance with radial basis function networks, while others showed some different results with multilayer perceptrons. This paper compares the...