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Download Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications (The Springer International Series in Engineering and Computer Science) fb2

by Nicolaos Karayiannis,Anastasios N. Venetsanopoulos

  • ISBN: 0792392973
  • Category: Technology
  • Author: Nicolaos Karayiannis,Anastasios N. Venetsanopoulos
  • Subcategory: Computer Science
  • Other formats: rtf lit lrf txt
  • Language: English
  • Publisher: Springer; 1993 edition (December 31, 1992)
  • Pages: 440 pages
  • FB2 size: 1271 kb
  • EPUB size: 1898 kb
  • Rating: 4.4
  • Votes: 369
Download Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications (The Springer International Series in Engineering and Computer Science) fb2

The Springer International Series in Engineering and Computer Science. Fast Learning Algorithms for Neural Networks.

The Springer International Series in Engineering and Computer Science. 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.

oceedings{icialNN, title {Artificial neural networks - learning algorithms, performance . 6. Performance Evaluation of Single-Layered Neural Networks.

oceedings{icialNN, title {Artificial neural networks - learning algorithms, performance evaluation, and applications}, author {Nicolaos B. Karayiannis and Anastasios N. Venetsanopoulos}, booktitle {The Kluwer international series in engineering and computer science}, year {1992} }. Nicolaos B. Karayiannis, Anastasios N. Venetsanopoulos. Published in. The Kluwer internationa. 992. 8. Applications of Neural Networks: A Case Study.

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Nicolaos Karayiannis, Anastasios N. Venetsanopoulos Neural Network Architectures and Learning Schemes. 9. ELEANNE Efficient LEarning Algorithms for Neural NEtworks. Neural Network Architectures and Learning Schemes. 87. 141. ALADIN Algorithms for Learning and Architecture DetermINation.

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The Kluwer international series in engineering and computer science . Efficient Learning Algorithms for Multi-layered Neural Networks . Computational Considerations . Experimental Results

The Kluwer international series in engineering and computer science ; SECS 0209) lnc1udes bibliographical references and index. ISBN ISBN (ebook) DOI, Neural networks (Computer science) 2. Machine learning. 1 Xlll . Overview Book Organization 4 2 Neural Network Architectures and Learning Schemes Introduction Feed-forward Neural Networks Feed-back Neural Networks Self-organizing Neural Networks Discussion. Experimental Results.

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The recent interest in artificial neural networks has motivated the publication of numerous books, including selections of. .Artificial Neural Networks : Learning Algorithms, Performance Evaluation, and Applications. by Anastasios N. Venetsanopoulos and Nicolaos Karayiannis.

Artificial Neural Networks : Learning Algorithms, Performance Evaluation, and Applications.

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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 neurophys­ 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 psy­ chologists have taken a top-down approach of studying brain func­ tions from the cognitive and behavioral level. While these two ap­ 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.

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