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A REDUNDANCY APPROACH TO CLASSIFIER TRAINING Mohamad Adnan Al-Alaoui1
 

Summary: A REDUNDANCY APPROACH TO CLASSIFIER TRAINING
Mohamad Adnan Al-Alaoui1
, Senior Member, IEEE, Rodolphe Mouci2
, and Mohamad Mansour3
1
Dept.of Electrical and Computer Engineering, American University of Beirut, email: adnan@aub.edu.lb
2
Central Bank of Lebanon, Beirut, Lebanon
3
Dept.of Electrical and Computer Engineering, University of Illinois, Urbana, Illinois
Abstract- The Al-Alaoui algorithm is a weighted
mean-square-error (MSE) approach to pattern
recognition. It employs redundancy, reintroducing
the erroneously classified samples to increase the
population of their corresponding classes. The
algorithm was originally developed for single-layer
neural networks. In this paper the algorithm is
extended to multilayer neural networks. It is also
shown that the application of the Al-Alaoui
algorithm to multilayer neural networks speeds up

  

Source: Al-Alaoui, Mohamad Adnan - Faculty of Engineering and Architecture, American University of Beirut, Lebanon

 

Collections: Engineering