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We employ high-order weights to extend the class of optimization problems that can be solved with neural networks. Hopfield and Tank networks are used; the associated energy function is a polynomial with order equal to the highest order weights in the network. As an example, we consider the problem of partitioning a graph into triangles. Simulation results(More)
  • T. Samad
  • IEEE 1988 International Conference on Neural…
  • 1988
A connectionist architecture, called RUBICON, for implementing rule-based systems, is described. RUBICON uses both distributed and local representations. Input and output are fully distributed, allowing the use of microfeatures for robust interfacing to the external world. All input units, however, are local. The local internal representation results in(More)
  • T. Samad
  • International 1989 Joint Conference on Neural…
  • 1989
Summary form only given, as follows. A class of neural network architectures is described that uses both distributed and local representation. The distributed representations are used for input and output, thereby enabling associative, noise-tolerant interaction with the environment. Internally, all representations are fully local. This simplifies weight(More)
Increasing internet usage and connectivity demands a network intrusion detection system combating cynical network attacks. Data mining therefore is a popular technique used by intrusion detection system to prevent the network attacks and classify the network events as either normal or attack. Our research study presents a wrapper approach for intrusion(More)
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