Jos J. Eggermont
Correlative Learning
A Basis for Brain and Adaptive Systems
Ebook (PDF Format)
This book bridges the communication gap between neuroscientists and engineers through the unifying theme of correlation-based learning Developing brain-style signal processing or machine learning algorithms has attracted many sharp minds from a range of disciplines. Now, coauthored by four researchers with varying backgrounds in signal processing, neuroscience, psychology, and computer science, Correlative Learning unifies the many cross-fertilized ideas in computational neuroscience and signal processing in a common language that will help engineers understand and appreciate the human brain as a highly sophisticated biosystem for building mo…
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Beschreibung
This book bridges the communication gap between neuroscientists and engineers through the unifying theme of correlation-based learning Developing brain-style signal processing or machine learning algorithms has attracted many sharp minds from a range of disciplines. Now, coauthored by four researchers with varying backgrounds in signal processing, neuroscience, psychology, and computer science, Correlative Learning unifies the many cross-fertilized ideas in computational neuroscience and signal processing in a common language that will help engineers understand and appreciate the human brain as a highly sophisticated biosystem for building more intelligent machines. First, the authors present the necessary neuroscience background for engineers, and then go on to relate the common intrinsic structures of the learning mechanisms of the brain to signal processing, machine learning, kernel learning, complex-valued domains, and the ALOPEX learning paradigm. This correlation-based approach to building complex, reliable (robust), and adaptive systems is vital for engineers, researchers, and graduate students from various fields of science and engineering. Figures, tables, worked examples, and case studies illustrate how to use computational tools for either helping to understand brain functions or fitting specific engineering applications, and a comprehensive bibliography covering over 1,000 references from major publications is included for further reading.
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Produktdetails
Weitere Autoren: Becker, Suzanna / Haykin, Simon / Chen, Zhe
- ISBN: 978-0-470-17144-8
- EAN: 9780470171448
- Produktnummer: 13923379
- Verlag: Wiley
- Sprache: Englisch
- Erscheinungsjahr: 2008
- Seitenangabe: 480 S.
- Plattform: PDF
- Masse: 14'702 KB
Über den Autor
Zhe Chen, PhD, is currently a Research Fellow in the Neuroscience Statistics Research Laboratory at Harvard Medical School. Simon Haykin, PhD, DSc, is a Distinguished University Professor in the Department of Electrical and Computer Engineering at McMaster University, Ontario, Canada. Jos J. Eggermont, PhD, is a Professor in the Departments of Physiology & Biophysics and Psychology at the University of Calgary, Alberta, Canada. Suzanna Becker, PhD, is a Professor in the Department of Psychology, Neuroscience, and Behavior at McMaster University, Ontario, Canada.
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