Fast learning and invariant object recognition : the sixth-generation breakthrough
著者
書誌事項
Fast learning and invariant object recognition : the sixth-generation breakthrough
（Sixth-generation computer technology series）
John Wiley, c1992
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注記
"A Wiley-Interscience publication."
Includes index
内容説明・目次
内容説明
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.
目次
- 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.
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