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Self-Organizing Maps
Teuvo Kohonen (Gebundene Ausgabe, Englisch)
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Beschreibung
The book we have at hand is the fourth monograph I wrote for Springer Verlag. The previous one named "Self-Organization and Associative Mem ory" (Springer Series in Information Sciences, Volume 8) came out in 1984. Since then the self-organizing neural-network algorithms called SOM and LVQ have become very popular, as can be seen from the many works re viewed in Chap. 9. The new results obtained… in the past ten years or so have warranted a new monograph. Over these years I have also answered lots of questions; they have influenced the contents of the present book. I hope it would be of some interest and help to the readers if I now first very briefly describe the various phases that led to my present SOM research, and the reasons underlying each new step. I became interested in neural networks around 1960, but could not in terrupt my graduate studies in physics. After I was appointed Professor of Electronics in 1965, it still took some years to organize teaching at the uni versity. In 1968 - 69 I was on leave at the University of Washington, and D. Gabor had just published his convolution-correlation model of autoasso ciative memory. I noticed immediately that there was something not quite right about it: the capacity was very poor and the inherent noise and crosstalk were intolerable. In 1970 I therefore sugge~ted the auto associative correlation matrix memory model, at the same time as J.A. Anderson and K. Nakano. Dieses Produkt haben wir gerade leider nicht auf Lager.
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Technische Daten
Erscheinungsdatum
02.03.1995
Sprache
Englisch
EAN
9783540586005
Herausgeber
Springer Berlin
Serien- oder Bandtitel
Springer Series in Information Sciences
Sonderedition
Nein
Autor
Teuvo Kohonen
Seitenanzahl
362
Einbandart
Gebundene Ausgabe
Bandzählung
30
Schlagwörter
unsupervised learning, self-organizing map, nervous system, calculus, pattern recognition, signal processing, neural modeling, telecommunications, data analysis, classification, algorithms, filters, neural networks, Mathematica, artificial neural network
Thema-Inhalt
PHVN - Biophysik
PHVD - Medizinische Physik
TJK - Nachrichtententechnik, Telekommunikation
PB - Mathematik
Höhe
235 mm
Breite
15.5 cm
-.-
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