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Titlebook: Hebbian Learning and Negative Feedback Networks; Colin Fyfe Book 2005 Springer-Verlag London 2005 Artificial neural networks.Data mining.E

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书目名称Hebbian Learning and Negative Feedback Networks
编辑Colin Fyfe
视频video
概述Concentrates on one specific architecture and learning rule which no other book does.State of the art in artificial neural networks which use Hebbian learning.A comparative study of a variety of techn
丛书名称Advanced Information and Knowledge Processing
图书封面Titlebook: Hebbian Learning and Negative Feedback Networks;  Colin Fyfe Book 2005 Springer-Verlag London 2005 Artificial neural networks.Data mining.E
描述This book is the outcome of a decade’s research into a speci?c architecture and associated learning mechanism for an arti?cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning. The research began with my own thesis at the University of Strathclyde, Scotland, under Professor Douglas McGregor which culminated with me being awarded a PhD in 1995 [52], the title of which was “Negative Feedback as an Organising Principle for Arti?cial Neural Networks”. Naturally enough, having established this theme, when I began to sup- vise PhD students of my own, we continued to develop this concept and this book owes much to the research and theses of these students at the Applied Computational Intelligence Research Unit in the University of Paisley. Thus we discuss work from • Dr. Darryl Charles [24] in Chapter 5. • Dr. Stephen McGlinchey [127] in Chapter 7. • Dr. Donald MacDonald [121] in Chapters 6 and 8. • Dr. Emilio Corchado [29] in Chapter 8. We brie?y discuss one simulation from the thesis of Dr. Mark Girolami [58] in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form [59]. We a
出版日期Book 2005
关键词Artificial neural networks; Data mining; Exploratory data analyis; Hebbian learning; Kernel; Machine lear
版次1
doihttps://doi.org/10.1007/b138856
isbn_softcover978-1-84996-945-1
isbn_ebook978-1-84628-118-1Series ISSN 1610-3947 Series E-ISSN 2197-8441
issn_series 1610-3947
copyrightSpringer-Verlag London 2005
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Multicollinearity and Partial Least Squaressingular (determinant equal to zero) correlation matrix. However, even when we do not reach this extreme, multicollinearity causes problems: because of the redundancy in the variables, we may have an ill-conditioned correlation matrix which is very prone to large variances which may be due to noise in the data set.
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Hebbian Learning and Negative Feedback Networks978-1-84628-118-1Series ISSN 1610-3947 Series E-ISSN 2197-8441
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