This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Data Representation for Diagnostic Neural Networks
October 1992 (vol. 7 no. 5)
pp. 43-44, 47, 49, 51-53

A paradigm for diagnostic neural network systems that emphasizes informative data representation and encoding and uses generic preprocessing techniques to extract knowledge from database records is discussed. The proposed diagnostic system differs from other approaches to automatic knowledge extraction in the following ways: by emphasizing the importance of intelligent encoding and preprocessing of raw data, rather than classifications; by demonstrating the importance of making a clear distinction between diagnostic and classification tasks; and by providing a generic, uniform representation for data records comprising interdependent, heterogeneous features. The correlation matrix memory (CMM), a linear system with a single-layer of input-output connections, that is used as the neural network system's classifier is described. The limitations of the learning system are discussed.

Citation:
Vladimir Cherkassky, Hossein Lari-Najafi, "Data Representation for Diagnostic Neural Networks," IEEE Intelligent Systems, vol. 7, no. 5, pp. 43-44, 47, 49, 51-53, Oct. 1992, doi:10.1109/64.163672
Usage of this product signifies your acceptance of the Terms of Use.