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Independent Component Analysis: A Tutorial Introduction (A Bradford Book)
USD28,15
Aproximadamente24,29 EUR
Estado:
En muy buen estado
Libro que se ha leído y que no tiene un aspecto nuevo, pero que está en un estado excelente. No hay desperfectos visibles en la tapa y se incluye sobrecubierta, si procede, para las tapas duras. Todas las páginas están en perfecto estado, sin arrugas ni roturas y no falta ninguna. El texto no está subrayado ni resaltado de forma alguna, y no hay anotaciones en los márgenes. Puede presentar marcas de identificación mínimas en la contraportada o las guardas. Muy poco usado. Consulta el anuncio del vendedor para obtener más información y la descripción de cualquier posible imperfección.
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Ubicado en: Little Falls, New Jersey, Estados Unidos
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Entrega prevista entre el jue. 7 ago. y el jue. 14 ago. a 94104
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N.º de artículo de eBay:306282735412
Última actualización el 03 ago 2025 00:09:36 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- Release Year
- 2004
- Book Title
- Independent Component Analysis: A Tutorial Introduction (A Bra...
- ISBN
- 9780262693158
Acerca de este producto
Product Identifiers
Publisher
MIT Press
ISBN-10
0262693151
ISBN-13
9780262693158
eBay Product ID (ePID)
30479995
Product Key Features
Number of Pages
200 Pages
Publication Name
Independent Component Analysis : a Tutorial Introduction
Language
English
Subject
Probability & Statistics / Multivariate Analysis, Neural Networks, Cognitive Psychology & Cognition, Neuropsychology
Publication Year
2004
Type
Textbook
Subject Area
Mathematics, Computers, Psychology
Format
Trade Paperback
Dimensions
Item Height
0.5 in
Item Weight
12.3 Oz
Item Length
8.8 in
Item Width
7.3 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2004-042589
Reviews
"This fantastic book provides a broad introduction to both the theory and applications of independent component analysis. I recommend it to any student interested in exploring this emerging field." -Martin J. McKeown, Associate Professor of Medicine (Neurology), University of British Columbia
Dewey Edition
22
Grade From
College Graduate Student
Illustrated
Yes
Dewey Decimal
006.3/2
Synopsis
A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab computer code examples. Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions. In Independent Component Analysis , Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method. An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls "the mathematical nuts and bolts" of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA. Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code., A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab computer code examples. Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions. In Independent Component Analysis , Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method. An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls the mathematical nuts and bolts of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA. Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code., A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab computer code examples.
LC Classification Number
QA76.87.S78 2004
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ZBK Books
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- 2***4 (144)- Votos emitidos por el comprador.Mes pasadoCompra verificadaFirst time buying from this seller and All went great. Great selection of DVDs and a buy 2 get 1 Free deals. shipped fast and well wrapped up for the journey, arrived early then they listed it to and the price on all three was great. Arrived just as they described each one to be.
- l***t (453)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaItem arrived as stated in used but good condition. It was a great price for used. A new copy would have cost a lot more. It was shipped out fast and arrived in secure envelope. Seller did a great job with this entire transaction. I’m very happy with seller and would order from them again. Thank you seller, great job. A+. JL
- s***t (182)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaThere was a mixup during packaging where a paperback copy was picked and shipped for a hard cover copy that was ordered. I asked the seller about it and they honestly replied that the mistake was on their end. A full refund for the item was sent immediately and I will not hesitate to order more from ZBK. Very responsive and they stand behind their items and make good on any issues.