Fast learning and invariant object recognition : the sixth-generation breakthrough
Author(s)
Bibliographic Information
Fast learning and invariant object recognition : the sixth-generation breakthrough
(Sixth-generation computer technology series)
John Wiley, c1992
Available at 26 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
"A Wiley-Interscience publication."
Includes index
Description and Table of Contents
Description
Learning, generalization, seeing and recognition are the major features of natural intelligence. This guide describes man-made systems that integrate these features, including high-speed architectures and system organizations suitable for industrial applications.
Table of Contents
- FAST LEARNING
- Learning to See (A. Franich & B. Sou ek)
- Fast Back Propagation with Adaptive Decoupled Momentum (A. Minai & R. Williams)
- Second Order Gradient Methods in Back Propagation (S. Piramuthu)
- Constructing Efficient Features for Back Propagation (S. Piramuthu)
- How Slow Processes Can Think Fast in Concurrent Logic?
- (F. Kozato & G. Ringwood)
- How to Deal with Multiple Possible Generalizers (D. Wolpert)
- Infinitesimal Versus Discrete Methods in Neural Network Synthesis (A. Albrecht)
- Systolic Array Implementation of Neural Networks (J. Chung, et al.)
- INVARIANT OBJECT RECOGNITION
- Translation Invariant Neural Networks (S. Wilson)
- Higher Order Neural Networks in Position, Scale, and Rotation Invariant Object Recognition (L. Spirkovska & M. Reid)
- Visual Tracking with Object Classification: Neural Network Approach (A. Dobnikar, et al.)
- A Neural Network Approach to Landmark Based Shape Recognition (N. Ansari & K. Li)
- Directed and Undirected Segmentation of Three-Dimensional Ultrasonograms (R. Silverman)
- Implementation of Neural Networks on Parallel Architectures and Invariant Object Recognition (M. Misra & V. Prasanna)
- Index.
by "Nielsen BookData"