Supervised training of a neural network for classification via successive modification of the training data - an experimental study
A method for training of an ML network for classification has been proposed by us in [3,4]. It searches for the non-linear discriminant functions corresponding to several small local minima of the objective function. This paper presents a comparative study of our method and conventional training with random initialization of the weights. Experiments with a synthetic data set and the data set of an OCR problem are discussed. The results obtained confirm the efficacy of our method which finds solutions with lower misclassification errors than does conventional training.
KeywordsNeural networks for classification auto-associative network projection pursuit structure removal discriminant analysis statistical pattern recognition
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