Identifying the functional roles of elements of a neural network is one of the first challenges in understanding neural information processing. Aiming at this goal, lesion studies have been used in neuroscience, most of which employing single lesions and hence, limited in their ability to reveal...

The FIRST survey (Faint Images of the Radio Sky at Twenty-cm) is scheduled to cover 10,000 square degrees of the northern and southern galactic caps. Until recently, astronomers classified radio-emitting galaxies through a visual inspection of FIRST images. Besides being subjective, prone to...

An alternative model to class conditional probability estimation for classification problems is introduced. The model assumes a deterministic mapping between input space and the set of classes, training data consists of probability distributions of the classes at the measurements points. A...

This paper proposes a new architecture for neural networks where the hidden layer is completely replaced by what is referred to as a 'principal component layer'. This layer consists of only a few selected principal components that perform the function of hidden nodes. Then, an algorithm based on...

This paper presents a Hopfield neural network model for matching features invariant to projective transformation. The projective invariance has been firstly embedded into the compatibility constraint, such that the problem of finding point correspondences can be formulated as minimizing the...

It is well known that multilayer neural networks with generalized delta rule (GDR) can map any arbitrary functions. The relationship between statistical linear regression and network learning has been attributed to the principal components which can be extracted from the input patterns along....

Automated incident detection is an essential component of a modern freeway traffic monitoring system. A number of neural network-based incident detection models have been tested independently over the past decade. This paper evaluates the adaptability of three promising neural network models for...