Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections (Machine Intelligence and Pattern Recognition)

K. Sethi, Ishwar

ISBN 10: 0444887407 / ISBN 13: 9780444887405

Published by Elsevier Science Ltd, 1991

About this Item

286 Seiten ex Library Book aus einer wissenschafltichen Bibliothek Sprache: Englisch Gewicht in Gramm: 969.

Seller Inventory # 302600

Bibliographic Details

• Title: Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections (Machine Intelligence and Pattern Recognition)
• Publisher: Elsevier Science Ltd
• Publication Date: 1991
• Language: English
• ISBN 10: 0444887407
• ISBN 13: 9780444887405
• Binding: Gebundene Ausgabe
• Condition: Gut
• Weight: 969 grams
• Seller catalogs:
  • Alle Bücher

About this title

Synopsis

With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.

Review

...a useful book... to be recommended to both those working in statistical pattern recognition as well as those in neural networks interested in the growing relationships between these fields.
IEEE Transactions on Neural Networks