Methods for binary multidimensional scaling
Neural Computation
Improved system for object detection and star/galaxy classification via local subspace analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
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Rule-based systems have been studied for nearly two decades in applications such as geographical information systems (GIS) and metadata catalog systems. Recovering large data sets that are not well organized is a challenge that imposes constraints on applications. These constraints include utilizing huge amounts of memory, consuming excessive amounts of time, and the risk of exceeding these resources, thus causing instability. This work examines a novel approach to provide a large unorganized data set by deriving a rule-based system that regulates web page generation thereby improve cache performance and query generation. The trade-offs imposed by rule-based systems in terms of time to deliver content, memory consumption, and fault tolerance are also analyzed.