Theoretical Advances in Neural Computation and Learning

Vwani Roychowdhury

ISBN 10: 079239478X / ISBN 13: 9780792394785

Published by Springer US, Springer New York, 1994

About this Item

Druck auf Anfrage Neuware - Printed after ordering - For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an swers are needed to important fundamental questions such as (a) what can neu ral networks do that traditional computing techniques cannot, (b) how does the complexity of the network for an application relate to the complexity of that problem, and (c) how much training data are required for the resulting network to learn properly Everyone working in the field has attempted to answer these questions, but general solutions remain elusive. However, encouraging progress in studying specific neural models has been made by researchers from various disciplines.

Seller Inventory # 9780792394785

Bibliographic Details

• Title: Theoretical Advances in Neural Computation and Learning
• Publisher: Springer US, Springer New York
• Publication Date: 1994
• Language: English
• ISBN 10: 079239478X
• ISBN 13: 9780792394785
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 32D millimeters 241 millimeters width by 160 millimeters height by 32 millimeters depth
• Weight: 904 grams

About this title

Synopsis

For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda­ tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu­ robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin­ ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an­ swers are needed to important fundamental questions such as (a) what can neu­ ral networks do that traditional computing techniques cannot, (b) how does the complexity of the network for an application relate to the complexity of that problem, and (c) how much training data are required for the resulting network to learn properly? Everyone working in the field has attempted to answer these questions, but general solutions remain elusive. However, encouraging progress in studying specific neural models has been made by researchers from various disciplines.