000 03698nam a22005415i 4500
001 978-1-84628-118-1
003 DE-He213
005 20161121230524.0
007 cr nn 008mamaa
008 100301s2005 xxk| s |||| 0|eng d
020 _a9781846281181
_9978-1-84628-118-1
024 7 _a10.1007/b138856
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aFyfe, Colin.
_eauthor.
245 1 0 _aHebbian Learning and Negative Feedback Networks
_h[electronic resource] /
_cby Colin Fyfe.
264 1 _aLondon :
_bSpringer London,
_c2005.
300 _aXVIII, 383 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Information and Knowledge Processing
505 0 _aSingle Stream Networks -- Background -- The Negative Feedback Network -- Peer-Inhibitory Neurons -- Multiple Cause Data -- Exploratory Data Analysis -- Topology Preserving Maps -- Maximum Likelihood Hebbian Learning -- Dual Stream Networks -- Two Neural Networks for Canonical Correlation Analysis -- Alternative Derivations of CCA Networks -- Kernel and Nonlinear Correlations -- Exploratory Correlation Analysis -- Multicollinearity and Partial Least Squares -- Twinned Principal Curves -- The Future.
520 _aThis 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 also must credit Cesar Garcia Osorio, a current PhD student, for the comparative study of the two Exploratory Projection Pursuit networks in Chapter 8. All of Chapters 3 to 8 deal with single stream arti?cial neural networks.
650 0 _aComputer science.
650 0 _aMathematical statistics.
650 0 _aArtificial intelligence.
650 0 _aComputer simulation.
650 0 _aPattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aPattern Recognition.
650 2 4 _aSimulation and Modeling.
650 2 4 _aComputer Science, general.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781852338831
830 0 _aAdvanced Information and Knowledge Processing
856 4 0 _uhttp://dx.doi.org/10.1007/b138856
912 _aZDB-2-SCS
950 _aComputer Science (Springer-11645)
999 _c500019
_d500019