An experimental analysis of the use of different neural models for the supervised classification of multisensor remote-sensing data is presented. Three types of neural classifiers are considered: the Multilayer Perceptron, a kind of Structured Neural Network, proposed by the authors, that allows the interpretation of the network operation, and a Probabilistic Neural Network. Furthermore, the k-...

Pattern recognition performance depends on variations during extraction, selection and classification stages. This paper presents an approach to feature selection by using genetic algorithms with regard to digital image recognition and quality control. Error rate and kappa coefficient were used for evaluating the genetic algorithm approach Neural networks were used for classification, involving...

ABSTRACT: Classification plays an important role in various fields like Object recognition, text categorization etc. Studying classifiers for purpose of estimating probability for a ce is crucial for classification .In this paper, we present a survey of four k Nearest Neighbour, Naive Bayes and Neural Network focusing on their merits and demerits.We will also shed light on combination of the ab...

Models for the integer quantum Hall effect: the network model, the Dirac equation, and a tight-binding Hamiltonian. Abstract We consider models for the plateau transition in the integer quantum Hall effect. Starting from the network model, we construct a mapping to the Dirac Hamiltonian in two dimensions. In the general case, the Dirac Hamiltonian has randomness in the mass, the scalar potentia...

Exemplar-based learning or, equally, nearest neighbour methods have recently gained interest from researchers in a variety of computer science domains because of the prevalence of large amounts of accessible data and storage capacity. In computer vision, these types of technique have been successful in several problems such as scene recognition, shape matching, image parsing, character recognit...