A boosting-based method for centers placement in radial basis function networks (RBFN) is proposed. Also, the influence of several methods for drawing random samples on the accuracy of RBFN is examined. The new method is compared to trivial, linear and non-linear regressors including the multilayer Perceptron and alternative RBFN learning algorithms and its advantages are demonstrated for learn...

Incremental Net Pro IncNet Pro with local learning feature and statistically controlled growing and pruning of the network is intro duced The architecture of the net is based on RBF networks Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm IncNet Pro is similar to the Resource Allocation Network described by Platt in the main idea of the expan...

5 We propose an advanced framework for the automatic configuration of 6 spectral dimensionality reduction methods. This is achieved by introducing, 7 first, the mutual information measure to assess the quality of discovered 8 embedded spaces. Secondly, unsupervised Radial Basis Function network is 9 designated for mapping between spaces where the learning process is derived 10 from graph theory...

An effective technique for identifying nonlinear discrete-time systems using raisedcosine radial basis function (RBF) networks is presented. Raised-cosine RBF networks are bounded-input bounded-output stable systems, and the network output is a continuously differentiable function of the past input and the past output. The evaluation speed of an n-dimensional raised-cosine RBF network is high b...

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...

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...