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Presentation transcript:

Use of such networks for pattern recognition: the input units represent the components of a feature vector, signals emited by output unit will be the values of the discriminant functions used for classification For a given input, one of the outputs “fire” (The output that gives you the highest value). So the input sample is classified to that category.

APPLICATION AREAS: -Character Recognition -Speech Recognition -Texture Segmentation -Biomedical Problems (Diagnosis) -Signal and Image Processing (Compression) -Business (Accounting, Marketing, Financial Analysis)

Y=output vector X=input vector T=Target output vector A feed-forward net A recurrent net

? (How to determine the weights w)

References R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, New York: John Wiley, N. Yalabık, “Pattern Classification with Biomedical Applications Course Material”, U. Halıcı, “Artificial Neural Networks Lecture Notes”, 2011.