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Neural Networks: Evolution, Topologies, Learning Algorithms and Applications

Neural Networks: Evolution, Topologies, Learning Algorithms and Applications
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Author(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India)
Copyright: 2012
Pages: 49
Source title: Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies
Source Author(s)/Editor(s): Vijay Kumar Mago (Simon Fraser University, Canada) and Nitin Bhatia (DAV College, India)
DOI: 10.4018/978-1-61350-429-1.ch024

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Abstract

These networks generally operate in two different modes, viz., supervised and unsupervised modes. The supervised mode of operation requires a supervisor to train the network with a training set of data. Networks operating in unsupervised mode apply topology preservation techniques so as to learn inputs. Representative examples of networks following either of these two modes are presented with reference to their topologies, configurations, types of input-output data and functional characteristics. Recent trends in this computing paradigm are also reported with due regards to the application perspectives.

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