Детальная информация
Название | New directions in statistical signal processing: from systems to brain |
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Другие авторы | Haykin Simon; Príncipe José C.; Sejnowski Terrence J.; McWhirter John |
Организация | IEEE Xplore (Online Service); MIT Press |
Выходные сведения | Cambridge, Massachusetts London, England: MIT Press, 2007 |
Коллекция | Электронные книги зарубежных издательств; Общая коллекция |
Тематика | Нейронные сети; Сигналы; Алгоритмы; нейробиология; MIT Press eBooks Library |
УДК | 004.032.26; 004.421; 577.25 |
Тип документа | Другой |
Тип файла | Другой |
Язык | Английский |
Права доступа | Доступ по паролю из сети Интернет (чтение, печать) |
Ключ записи | 6267276 |
Дата создания записи | 23.12.2015 |
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).
Количество обращений: 21
За последние 30 дней: 1