9781439815359 - subspace learning of neural networks (automation and control engineering, band 42) von lv, jian cheng; yi, zhang; zhou, jiliu (3 Ergebnisse)
Sprache: Englisch
Verlag: Taylor & Francis Group, 2010
- Hardcover
- Erstausgabe
Anbieter: Better World Books, Mishawaka, IN, USABetter World Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Gebraucht - Gut
EUR 66,68
Versand gratisVersand innerhalb von USAAnzahl: 1 verfügbar
Zustand: Very Good. 1st Edition. 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.
Sprache: Englisch
Verlag: CRC Press, 2010
- Hardcover
Anbieter: moluna, Greven, Deutschlandmoluna
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 180,58
EUR 48,99 VersandVersand von Deutschland nach USAAnzahl: Mehr als 20 verfügbar
Gebunden. Zustand: New. Jian Cheng LV and Zhang Yi are affiliated with the Machine Intelligence Lab of the College of Computer Science at Sichuan University. Jiliu Zhou is affiliated with the College of Computer Science at Sichuan University.Using real-life examp.
Sprache: Englisch
Verlag: Taylor & Francis Inc Sep 2010, 2010
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 222,81
EUR 62,49 VersandVersand von Deutschland nach USAAnzahl: 2 verfügbar
Buch. Zustand: Neu. Neuware - 'Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research…offering a host of practical applications. They demonstrate ways to extend the use of algorithms to fields such as encryption communication, data mining, computer vision, and signal and image processing to name just a few. The brilliance of the work lies with how it coherently builds a theoretical understanding of the convergence behavior of subspace learning algorithms through a summary of chaotic behaviors'--.
