検索条件入力書誌詳細関連資料一覧:(本学所蔵) > Neural network design and the complexity of learning
書誌情報:Neural network design and the complexity of learning
J. Stephen Judd
Cambridge, Mass. : MIT Press , c1990
1 online resource (150 p.) : ill.
WebCatPlus を見る
CiNii Books を見る


  


所蔵一覧
https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267404
巻号予約人数所在請求記号登録番号資料ID状態貸出区分備考 
1: electronic bk0オンライン 1A001631  利用可
電子書籍 

選択行を:  

書誌詳細
刊年1990
G/SMDリモートファイル
形態1 online resource (150 p.) : ill.
シリーズ名Neural network modeling and connectionism
注記"A Bradford book."
Includes bibliographical references (p. [137]-143) and index
Restricted to subscribers or individual electronic text purchasers
Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier.Judd looks beyond the scope of any one particular learning rule, at a level above the details of neurons. There he finds new issues that arise when great numbers of neurons are employed and he offers fresh insights into design principles that could guide the construction of artificial and biological neural networks.The first part of the book describes the motivations and goals of the study and relates them to current scientific theory. It provides an overview of the major ideas, formulates the general learning problem with an eye to the computational complexity of the task, reviews current theory on learning, relates the book's model of learning to other models outside the connectionist paradigm, and sets out to examine scale-up issues in connectionist learning.Later chapters prove the intractability of the general case of memorizing in networks, elaborate on implications of this intractability and point out several corollaries applying to various special subcases. Judd refines the distinctive characteristics of the difficulties with families of shallow networks, addresses concerns about the ability of neural networks to generalize, and summarizes the results, implications, and possible extensions of the work.J. Stephen Judd is Visiting Assistant Professor of Computer Science at The California Institute of Technology. Neural Network Design and the Complexity of Learning is included in the Network Modeling and Connectionism series edited by Jeffrey Elman
Also available in print
Mode of access: World Wide Web
Description based on PDF viewed 12/23/2015
URL:https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267404(Abstract with links to resource)
出版国アメリカ合衆国
標題言語英語
本文言語英語
著者情報Judd, J. Stephen
ISBN9780262276559(: electronic bk)
無効/取消ISBN9780585359342(: electronic bk)
件名LCSH:Artificialintelligence
LCSH:Computationalcomplexity
LCSH:Neuralcomputers
NCID6267404
IDENThttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267404

WebCatPlus を見る    CiNii Books を見る