Condizione: Very Good. This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. . Codice articolo 7719-9781108493703

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Data-Driven Computational Neuroscience: Machine Learning and Statistical Models

Bielza, Concha Concha Bielza, Pedro Larrañaga,

Editore: Cambridge University Press, 2020

ISBN 10: 110849370X ISBN 13: 9781108493703

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Da: Bahamut Media, Reading, Regno Unito

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Condizione: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee. Codice articolo 6545-9781108493703

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DATA-DRIVEN COMPUTATIONAL NEUROSCIENCE

CONCHA BIELZA , PEDRO LARRAñAGA

Editore: Cambridge University Press, 2020

ISBN 10: 110849370X ISBN 13: 9781108493703

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Da: Basi6 International, Irving, TX, U.S.A.

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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Codice articolo ABEJUNE24-323785

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Data-Driven Computational Neuroscience: Machine Learning and Statistical Models

Concha Bielza (Universidad Politecnica de Madrid)

Editore: Cambridge University Press, United Kingdom, Cambridge, 2020

ISBN 10: 110849370X ISBN 13: 9781108493703

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Da: WorldofBooks, Goring-By-Sea, WS, Regno Unito

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Paperback. Condizione: Very Good. Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Codice articolo GOR014300744

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