IEEE Transactions on Neural Networks

First Page of the Article

Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new types of learning algorithms (...

This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In particular, we review what is known about the shape of ...

In this paper, we extend the use of moderated outputs to the support vector machine (SVM) by making use of a relationship between SVM and the evidence framework. The moderated output is more in line with the Bayesian idea that the posterior weight ...

Successive overrelaxation (SOR) for symmetric linear complementarity problems and quadratic programs is used to train a support vector machine (SVM) for discriminating between the elements of two massive datasets, each with millions of points. Because ...

Most support vector (SV) methods proposed in the recent literature can be viewed in a unified framework with great flexibility in terms of the choice of the kernel functions and their constraints. We show that all these problems can be solved within a ...

We study the use of support vector machines (SVM) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data ...

Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM) can generalize well on difficult image classification ...

Biometric person identity authentication is gaining more and more attention. The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (...

It is well known that for a given sample size there exists a model of optimal complexity corresponding to the smallest prediction (generalization) error. Hence, any method for learning from finite samples needs to have some provisions for complexity ...

This paper proposes a backpropagation-based feedforward neural network for learning probability distributions of outputs conditioned on inputs using incoming input-output samples only. The backpropagation procedure is shown to locally minimize the ...

We present a hybrid system for managing both symbolic and sub-symbolic knowledge in a uniform way. Our aim is to solve problems where some gap in formal theories occurs which stops one from getting a fully symbolical solution. The idea is to use neural ...

A recurrent neural network, called the Lagrangian network, is presented for the kinematic control of redundant robot manipulators. The optimal redundancy resolution is determined by the Lagrangian network through real-time solution to the inverse ...

A robust backpropagation training algorithm with a dead zone scheme is used for the online tuning of the neural-network (NN) tracking control system. This assures the convergence of the multilayer NN in the presence of disturbance. It is proved in this ...

We extend the nearest prototype classifier to a generalized nearest prototype classifier (GNPC). The GNPC uses “soft” labeling of the prototypes in the classes, thereby encompassing a variety of classifiers. Based on how the prototypes are ...

This paper presents an axiomatic approach to soft learning vector quantization (LVQ) and clustering based on reformulation. The reformulation of the fuzzy c-means (FCM) algorithm provides the basis for reformulating entropy-constrained fuzzy clustering (...

Picture compression algorithms, using a parallel structure of neural networks, have recently been described. Although these algorithms are intrinsically robust, and may therefore be used in high noise environments, they suffer from several drawbacks: ...

A general algorithm for blind separation of uniformly distributed signals is presented. First, maximum likelihood equations are obtained for dealing with this task. It is difficult to obtain a closed form maximum likelihood solution for arbitrary mixing ...

In a typical linear data compression system the representation variables resulting from the coding operation are assumed totally reliable and therefore the solution in the mean-squared-error sense is an orthogonal projector to the so-called principal ...

Very often the input variables for neural-network predictions contain measurement errors. In particular, this may happen because the original input variables are often not available at the time of prediction and have to be replaced by predicted values ...

We study the application of neural networks to modeling the blood glucose metabolism of a diabetic. In particular we consider recurrent neural networks and time series convolution neural networks which we compare to linear models and to nonlinear ...

Addresses the problems of fault diagnosis in complex multicircuit transmission systems, in particular those arising due to mutual coupling between the two parallel circuits under different fault conditions; the problems are compounded by the fact that ...

A subthreshold MOS integrated circuit (IC) is designed and fabricated for implementing a competitive neural network of the Lotka-Volterra (LV) type which is derived from conventional membrane dynamics of neurons and is used for the selection of external ...

We propose an adaptive procedure that enables a spiking neuron, whether artificial or biological, to make optimal use of its dynamic range and gain. We discuss an analog electronic circuit implementation of this algorithm using a biologically realistic ...

Presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization ...

We propose a method for decomposing pattern classification problems based on the class relations among training data. By using this method, we can divide a K-class classification problem into a series of (₂^K) two-class problems. These two-class problems ...

The complex dynamics of the chaotic neural networks makes it possible for them to escape from local minimum of the simple gradient descent neurodynamics. We use a transiently chaotic neural network to detect the CDMA multiuser signals and hence obtain ...