C o m p a ra t iv e S tu d y of th e C a s c a d e-C o r re la t io n A rc h ite c tu re in P a t te r n R e c o g n itio n A p p lic a tio n s Abstract In this work, an experimental evaluation of the cascade-correlation architecture is carried out in different bench-marking pattern recognition problems. An extensive experimental framework is developed to establish a comparison between the casc...

This paper presents a set of optimizations in learning algorithms commonly used for training radial basis function neural networks. These optimizations are applied to a RBF neural network used in identifying helicopter types processing their rotor sounds. The first method uses an optimum learning rate in each iteration of train process. This method increases the speed of learning process and al...

This paper presents a preliminary exploration showing the surprising effect of extreme parameter values used by Support Vector Machine (SVM) classifiers for identifying objects in images. The Radial Basis Function (RBF) kernel used with SVM classifiers is considered to be a state-of-the-art approach in visual object classification. Standard tuning approaches apply a relative narrow window of va...

In the formulation of radial basis function (RBF) network, there are three factors mainly considered, i.e., centers, widths, and weights, which significantly affect the performance of the network. Within thus three factors, the placement of centers is proved theoretically and practically to be critical. In order to obtain a compact network, this paper presents an improved clustering (IC) scheme...

We propose a nonlinear image restoration method that uses the generalized radial basis function network (GRBFN) and a regularization method. The GRBFN is used to estimate the nonlinear blurring function. The regularization method is used to recover the original image from the nonlinearly degraded image. We alternately use the two estimation methods to restore the original image from the degrade...

In this paper a new method for fast initialization of radial basis function (RBF) networks is proposed. A grid of possible positions and widths for the basis functions is de ned and new nodes to the RBF network are introduced one at the time. The de nition of the grid points is done in a speci c way which leads to algorithms which are computationally inexpensive due to the fact that intermediat...

Speaker verification based on phone modelling is examined in this paper. Phone modelling is attractive, because different phonemes have different levels of usefulness for speaker recognition, and because phone modelling essentially makes a speaker verification algorithm text independent. The speaker verification system used here is based on a two stage approach, where speech recognition (segmen...

In this paper we propose several sets of new features for protein fold prediction. The first feature set consisting of 47 features uses only the sequence information. We also define four different sets of features based on hydrophobicity of amino acids. Each such set has 400 features which are motivated by folding energy modeling. To define these features we have considered pair-wise amino acid...

Radial basis function networks are usually trained according to a three-stage procedure. In the literature, many papers are devoted to the estimation of the position of Gaussian kernels, as well as the computation of the weights. Meanwhile, very few focus on the estimation of the kernel widths. In this paper, first, we develop a heuristic to optimize the widths in order to improve the generaliz...

Recently, the open-set recognition problem has received more attention by the machine learning community given that most classification problems in practice require an open-set treatment. Thus far, many classifiers were mostly developed for the closed-set scenario, i.e., the scenario of classification in which it is assumed that all test samples belong to one of the classes the classifier was t...