Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections

I. K. Sethi et Anil K. Jain

ISBN 10: 0444887407 / ISBN 13: 9780444887405

Published by Elsevier Science Ltd, 1991

About this Item

Ancien livre de bibliothèque. Couverture différente. Edition 1991. Tome 11. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Different cover. Edition 1991. Volume 11. Ammareal gives back up to 15% of this item's net price to charity organizations. Seller Inventory # E-854-193

Bibliographic Details

Title: Artificial Neural Networks and Statistical ...

Publisher: Elsevier Science Ltd

Publication Date: 1991

Binding: Hardcover

Condition: Très bon

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.