Scope This workshop reviewed and classified the various models which have emerged from the general concept of selective attention and context dependency, and sought to identify their commonalities. It was concluded that the motivation and mechanism of these functional models are "efficiency" and ''factoring'', respectively. The workshop focused on computational models of selective attention and context dependency within the realm of neural networks. We treated only ''functional'' models; computational models of biological neural systems, and symbolic or rule-based systems were omitted from the discussion. Presentations Thomas H. Hildebrandt presented the results of his recent survey of the literature on functional models of selective attention and context dependency.

Given such a classification task in most cases it is not too difficult to devise a network architecture that is capable of learning the input-output relation as represented by a number of training examples.

Scope This workshop reviewed and classified the various models which have emerged from the general concept of selective attention and context dependency, and sought to identify their commonalities. It was concluded that the motivation and mechanism of these functional models are "efficiency" and ''factoring'', respectively. The workshop focused on computational models of selective attention and context dependency within the realm of neural networks. We treated only ''functional'' models; computational models of biological neural systems, and symbolic or rule-based systems were omitted from the discussion. Presentations Thomas H. Hildebrandt presented the results of his recent survey of the literature on functional models of selective attention and context dependency.

Given such a classification task in most cases it is not too difficult to devise a network architecture that is capable of learning the input-output relation as represented by a number of training examples.

Scope This workshop reviewed and classified the various models which have emerged from the general concept of selective attention and context dependency, and sought to identify their commonalities. It was concluded that the motivation and mechanism ofthese functional models are "efficiency" and ''factoring'', respectively. The workshop focused on computational models of selective attention and context dependency withinthe realm of neural networks. We treated only ''functional'' models; computational models of biological neural systems, and symbolic or rule-based systems were omitted from the discussion. Presentations Thomas H. Hildebrandt presented the results of his recent survey of the literature onfunctional models of selective attention and context dependency.

Given such a classification task in most cases it is not too difficult to devise a network architecture that is capable of learning the input-output relation as represented by a number of training examples.

Efficient indexing schemes have influenced the acceptance of production systems in the industrial world. However, in embedded-control systems, production systems have not been applied intensively because of their nondeterministic run-time behavior. Thus, nonpredictability of response times is a major obstacle to the widespread use of expert systems in the real-time domain. The RETE and TREAT algorithms and their offspring play a major role in the implementation of efficient pattern-matching systems.

(Stankovic and Ramamithram 1990). Such systems are considered intelligent switching that is used in the railway stations when they are able to perform complex of three European countries. Parts of the control actions in response to the sensed environment. Time is a valuable resource that is lost when Years ago, a request came from the railway the system must reason about actions before authorities for a system that could guarantee performing them. This requirement is understandable on two levels: the problem-space level because in typical real-time systems, and the knowledge base level (Tambe and scheduling is mostly based on worstcase Newell 1988). With the problem-space level, execution times of the tasks involved. Usually, a sequence of steps is required the railway authorities.

In this work we apply a texture classification network to remote sensing image analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain. 1 INTRODUCTION In this work we apply a texture classification network to remote sensing image analysis. The goal is to segment the input image into homogeneous textured regions and identify each region as one of a prelearned library of textures, e.g.

In this work we apply a texture classification network to remote sensing image analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain. 1 INTRODUCTION In this work we apply a texture classification network to remote sensing image analysis. The goal is to segment the input image into homogeneous textured regions and identify each region as one of a prelearned library of textures, e.g.