β Scribed by Simon Haykin, Jose C. Principe, Terrence J. Sejnowski, John McWhirter (Editors)
No coin nor oath required. For personal study only.
Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines.The book's 14 chapters, almost evenly divided between signal processing and neural computation, begin with the brain and move on to communication, signal processing, and learning systems. They examine such topics as how computational models help us understand the brain's information processing, how an intelligent machine could solve the "cocktail party problem" with "active audition" in a noisy environment, graphical and network structure modeling approaches, uncertainty in network communications, the geometric approach to blind signal processing, game-theoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs).
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Leading researchers in signal processing and neural computation present work aimed at promoting the interaction and cross-fertilization between the two fields.
Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elabo
ΠΠ·Π΄Π°ΡΠ΅Π»ΡΡΡΠ²ΠΎ MIT Press, 2007, -525 pp.<div class="bb-sep"></div>In the course of some 60 to 65 years, going back to the 1940s, signal processing and neural computation have evolved into two highly pervasive disciplines. In their own individual ways, they have significantly influenced many other disc
The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learnin