New directions in statistical signal processing: from systems to brain
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass.
MIT Press
c2007
|
| Schriftenreihe: | Neural information processing series
|
| Schlagworte: | |
| Beschreibung: | vi, 514 p. |
| ISBN: | 0262083485 9780262083485 |
Internformat
MARC
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| 020 | |a 0262083485 |c alk. paper |9 0-262-08348-5 | ||
| 020 | |a 9780262083485 |c alk. paper |9 978-0-262-08348-5 | ||
| 035 | |a (ZDB-38-ESG)ebr10173712 | ||
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| 082 | 0 | |a 612.8/2 |2 22 | |
| 245 | 1 | 0 | |a New directions in statistical signal processing |b from systems to brain |c edited by Simon Haykin ... [et al.] |
| 264 | 1 | |a Cambridge, Mass. |b MIT Press |c c2007 | |
| 300 | |a vi, 514 p. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Neural information processing series | |
| 505 | 8 | |a Includes bibliographical references (p. [465]-508) and index | |
| 505 | 8 | 0 | |t Modeling the mind : from circuits to systems |r Suzanna Becker -- |t Empirical statistics and stochastic models for visual signals |r David Mumford -- |g The |t machine cocktail party problem |r Simon Haykin, Zhe Chen -- |t Sensor adaptive signal processing of biological nanotubes (ion channels) at macroscopic and nano scales |r Vikram Krishnamurthy -- |t Spin diffusion : a new perspective in magnetic resonance imaging |r Timothy R. Field -- |t What makes a dynamical system computationally powerful? |r Robert Legenstein, Wolfgang Maass -- |g A |t variational principle for graphical models |r Martin J. Wainwright, Michael I. Jordan -- |t Modeling large dynamical systems with dynamical consistent neural networks |r Hans-Georg Zimmermann ... [et al.] -- |t Diversity in communication : from source coding to wireless networks |r Suhas N. Diggavi -- |t Designing patterns for easy recognition : information transmission with low-density parity-check codes |r Frank R. Kschischang, Masoud Ardakani -- |t Turbo processing |r Claude Berrou, Charlotte Langlais, Fabrice Seguin -- |t Blind signal processing based on data geometric properties |r Konstantinos Diamantaras -- |t Game-theoretic learning |r Geoffrey J. Gordon -- |t Learning observable operator models via the efficient sharpening algorithm |r Herbert Jaeger ... [et al.] |
| 650 | 4 | |a Neural networks (Neurobiology) | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 4 | |a Signal processing |x Statistical methods | |
| 650 | 4 | |a Neural computers | |
| 700 | 1 | |a Haykin, Simon S. |d 1931- |e Sonstige |4 oth | |
| 912 | |a ZDB-38-ESG | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030231503 | ||
Datensatz im Suchindex
| _version_ | 1804178341704499200 |
|---|---|
| any_adam_object | |
| author_additional | Suzanna Becker -- David Mumford -- Simon Haykin, Zhe Chen -- Vikram Krishnamurthy -- Timothy R. Field -- Robert Legenstein, Wolfgang Maass -- Martin J. Wainwright, Michael I. Jordan -- Hans-Georg Zimmermann ... [et al.] -- Suhas N. Diggavi -- Frank R. Kschischang, Masoud Ardakani -- Claude Berrou, Charlotte Langlais, Fabrice Seguin -- Konstantinos Diamantaras -- Geoffrey J. Gordon -- Herbert Jaeger ... [et al.] |
| building | Verbundindex |
| bvnumber | BV044836640 |
| collection | ZDB-38-ESG |
| contents | Includes bibliographical references (p. [465]-508) and index Modeling the mind : from circuits to systems Empirical statistics and stochastic models for visual signals machine cocktail party problem Sensor adaptive signal processing of biological nanotubes (ion channels) at macroscopic and nano scales Spin diffusion : a new perspective in magnetic resonance imaging What makes a dynamical system computationally powerful? variational principle for graphical models Modeling large dynamical systems with dynamical consistent neural networks Diversity in communication : from source coding to wireless networks Designing patterns for easy recognition : information transmission with low-density parity-check codes Turbo processing Blind signal processing based on data geometric properties Game-theoretic learning Learning observable operator models via the efficient sharpening algorithm |
| ctrlnum | (ZDB-38-ESG)ebr10173712 (OCoLC)77521428 (DE-599)BVBBV044836640 |
| dewey-full | 612.8/2 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 612 - Human physiology |
| dewey-raw | 612.8/2 |
| dewey-search | 612.8/2 |
| dewey-sort | 3612.8 12 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| format | Electronic eBook |
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| id | DE-604.BV044836640 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:02:26Z |
| institution | BVB |
| isbn | 0262083485 9780262083485 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030231503 |
| oclc_num | 77521428 |
| open_access_boolean | |
| physical | vi, 514 p. |
| psigel | ZDB-38-ESG |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | MIT Press |
| record_format | marc |
| series2 | Neural information processing series |
| spelling | New directions in statistical signal processing from systems to brain edited by Simon Haykin ... [et al.] Cambridge, Mass. MIT Press c2007 vi, 514 p. txt rdacontent c rdamedia cr rdacarrier Neural information processing series Includes bibliographical references (p. [465]-508) and index Modeling the mind : from circuits to systems Suzanna Becker -- Empirical statistics and stochastic models for visual signals David Mumford -- The machine cocktail party problem Simon Haykin, Zhe Chen -- Sensor adaptive signal processing of biological nanotubes (ion channels) at macroscopic and nano scales Vikram Krishnamurthy -- Spin diffusion : a new perspective in magnetic resonance imaging Timothy R. Field -- What makes a dynamical system computationally powerful? Robert Legenstein, Wolfgang Maass -- A variational principle for graphical models Martin J. Wainwright, Michael I. Jordan -- Modeling large dynamical systems with dynamical consistent neural networks Hans-Georg Zimmermann ... [et al.] -- Diversity in communication : from source coding to wireless networks Suhas N. Diggavi -- Designing patterns for easy recognition : information transmission with low-density parity-check codes Frank R. Kschischang, Masoud Ardakani -- Turbo processing Claude Berrou, Charlotte Langlais, Fabrice Seguin -- Blind signal processing based on data geometric properties Konstantinos Diamantaras -- Game-theoretic learning Geoffrey J. Gordon -- Learning observable operator models via the efficient sharpening algorithm Herbert Jaeger ... [et al.] Neural networks (Neurobiology) Neural networks (Computer science) Signal processing Statistical methods Neural computers Haykin, Simon S. 1931- Sonstige oth |
| spellingShingle | New directions in statistical signal processing from systems to brain Includes bibliographical references (p. [465]-508) and index Modeling the mind : from circuits to systems Empirical statistics and stochastic models for visual signals machine cocktail party problem Sensor adaptive signal processing of biological nanotubes (ion channels) at macroscopic and nano scales Spin diffusion : a new perspective in magnetic resonance imaging What makes a dynamical system computationally powerful? variational principle for graphical models Modeling large dynamical systems with dynamical consistent neural networks Diversity in communication : from source coding to wireless networks Designing patterns for easy recognition : information transmission with low-density parity-check codes Turbo processing Blind signal processing based on data geometric properties Game-theoretic learning Learning observable operator models via the efficient sharpening algorithm Neural networks (Neurobiology) Neural networks (Computer science) Signal processing Statistical methods Neural computers |
| title | New directions in statistical signal processing from systems to brain |
| title_alt | Modeling the mind : from circuits to systems Empirical statistics and stochastic models for visual signals machine cocktail party problem Sensor adaptive signal processing of biological nanotubes (ion channels) at macroscopic and nano scales Spin diffusion : a new perspective in magnetic resonance imaging What makes a dynamical system computationally powerful? variational principle for graphical models Modeling large dynamical systems with dynamical consistent neural networks Diversity in communication : from source coding to wireless networks Designing patterns for easy recognition : information transmission with low-density parity-check codes Turbo processing Blind signal processing based on data geometric properties Game-theoretic learning Learning observable operator models via the efficient sharpening algorithm |
| title_auth | New directions in statistical signal processing from systems to brain |
| title_exact_search | New directions in statistical signal processing from systems to brain |
| title_full | New directions in statistical signal processing from systems to brain edited by Simon Haykin ... [et al.] |
| title_fullStr | New directions in statistical signal processing from systems to brain edited by Simon Haykin ... [et al.] |
| title_full_unstemmed | New directions in statistical signal processing from systems to brain edited by Simon Haykin ... [et al.] |
| title_short | New directions in statistical signal processing |
| title_sort | new directions in statistical signal processing from systems to brain |
| title_sub | from systems to brain |
| topic | Neural networks (Neurobiology) Neural networks (Computer science) Signal processing Statistical methods Neural computers |
| topic_facet | Neural networks (Neurobiology) Neural networks (Computer science) Signal processing Statistical methods Neural computers |
| work_keys_str_mv | AT haykinsimons newdirectionsinstatisticalsignalprocessingfromsystemstobrain |