Supervised training of a neural network for classification via successive modification of the training data - An experimental study

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Abstract

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.

Original languageEnglish
Title of host publicationTasks and Methods in Applied Artificial Intelligence - 11 th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA-1998-AIE, Proceedings
EditorsMoonis Ali, Angel Pasqual del Pobil, Jose Mira
PublisherSpringer Verlag
Pages593-602
Number of pages10
ISBN (Print)3540645748, 9783540645740
DOIs
StatePublished - 1 Jan 1998
Event11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA-1998-AIE - Benicassim, Spain
Duration: 1 Jun 19984 Jun 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1416
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA-1998-AIE
Country/TerritorySpain
CityBenicassim
Period1/06/984/06/98

Keywords

  • Auto-associative network
  • Discriminant analysis
  • Neural networks for classification
  • Projection pursuit
  • Statistical pattern recognition
  • Structure removal

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