ΠΠ·Π΄Π°ΡΠ΅Π»ΡΡΡΠ²ΠΎ Imperial College Press, 2007, -322 pp.<div class="bb-sep"></div>The area of Neural computing that we shall discuss in this book represents a combination of techniques of classical optimization, statistics, and information theory. Neural network was once widely called artificial neural n
Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications
β Scribed by N. B. Karayiannis, A. N. Venetsanopoulos (auth.)
- Publisher
- Springer US
- Year
- 1993
- Tongue
- English
- Leaves
- 445
- Series
- The Springer International Series in Engineering and Computer Science 209
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
1.1 Overview We are living in a decade recently declared as the "Decade of the Brain". Neuroscientists may soon manage to work out a functional map of the brain, thanks to technologies that open windows on the mind. With the average human brain consisting of 15 billion neurons, roughly equal to the number of stars in our milky way, each receiving signals through as many as 10,000 synapses, it is quite a view. "The brain is the last and greatest biological frontier", says James Weston codiscoverer of DNA, considered to be the most complex piece of biological machinery on earth. After many years of research by neuroanatomists and neurophysΒ iologists, the overall organization of the brain is well understood, but many of its detailed neural mechanisms remain to be decoded. In order to understand the functioning of the brain, neurobiologists have taken a bottom-up approach of studying the stimulus-response characteristics of single neurons and networks of neurons, while psyΒ chologists have taken a top-down approach of studying brain funcΒ tions from the cognitive and behavioral level. While these two apΒ proaches are gradually converging, it is generally accepted that it may take another fifty years before we achieve a solid microscopic, intermediate, and macroscopic understanding of brain.
β¦ Table of Contents
Front Matter....Pages i-xv
Introduction....Pages 1-8
Neural Network Architectures and Learning Schemes....Pages 9-85
ELEANNE: Efficient LEarning Algorithms for Neural NEtworks....Pages 87-139
Fast Learning Algorithms for Neural Networks....Pages 141-193
ALADIN: Algorithms for Learning and Architecture DetermINation....Pages 195-218
Performance Evaluation of Single-layered Neural Networks....Pages 219-257
High-order Neural Networks and Networks with Composite Key Patterns....Pages 259-298
Applications of Neural Networks: A Case Study....Pages 299-315
Applications of Neural Networks: A Review....Pages 317-369
Future Trends and Directions....Pages 371-373
Back Matter....Pages 375-440
β¦ Subjects
Circuits and Systems;Statistical Physics, Dynamical Systems and Complexity;Signal, Image and Speech Processing;Electrical Engineering
π SIMILAR VOLUMES
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