9783540679219 - Self-Organizing Maps: Third Edition (Springer Series in Information Sciences, 30, Band 30) von Kohonen, Teuvo (6 Ergebnisse)

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Zustand: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: Good. 3. Good; Softcover; 3rd Edition; Moderate wear to the covers; Clean textblock edges; Light handwriting to about 20 pagesThe endpapers and text pages are all clean and unmarked; Good binding; This book will be shipped in a sturdy cardboard box with foam padding; Medium Format (8.5" - 9.75" tall); 2.0 lbs; 2000, Springer-Verlag Publishing; 521 pages; "Self-Organizing Maps," by Teuvo Kohonen.

Paperback. Zustand: Sehr gut. Third Edition. 528 pp. Very well preserved copy with only slight signs of wear 327 Sprache: Englisch Gewicht in Gramm: 1090.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Since the second edition of this book came out in early 1997, the number of scientific papers published on the Self-Organizing Map (SOM) has increased from about 1500 to some 4000. Also, two special workshops dedicated to the SOM have been organized, not to mention numerous SOM sessions in neural network conferences. In view of this growing interest it was felt desirable to make extensive revisions to this book. They are of the following nature. Statistical pattern analysis has now been approached more carefully than earlier. A more detailed discussion of the eigenvectors and eigenvalues of symmetric matrices, which are the type usually encountered in statistics, has been included in Sect. 1.1.3: also, new probabilistic concepts, such as factor analysis, have been discussed in Sect. 1.3.1. A survey of projection methods (Sect. 1.3.2) has been added, in order to relate the SOM to classical paradigms. Vector Quantization is now discussed in one main section, and derivation of the pointdensity of the codebook vectors using the calculus of variations has been added, in order to familiarize the reader with this otherwise com plicated statistical analysis. It was also felt that the discussion of the neural-modeling philosophy should include a broader perspective of the main issues. A historical review in Sect. 2.2, and the general philosophy in Sects. 2.3, 2.5 and 2.14 are now expected to especially help newcomers to orient themselves better amongst the profusion of contemporary neural models.