Theory of neural information processing systems /:
"Theory of Neural Information Processing Systems provides an explicit, coherent, and up-to-date account of the modern theory of neural information processing systems. It has been carefully developed for graduate students from any quantitative discipline, including mathematics, computer science,...
Gespeichert in:
| 1. Verfasser: | |
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| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Oxford :
Oxford University Press,
2005.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "Theory of Neural Information Processing Systems provides an explicit, coherent, and up-to-date account of the modern theory of neural information processing systems. It has been carefully developed for graduate students from any quantitative discipline, including mathematics, computer science, physics, engineering, biology, and has been thoroughly class-tested by the authors over a period of some 8 years. Exercises are presented throughout the text and notes on historical background and further reading guide the students into the literature. All mathematical details are included and appendices provide further background material, including probability theory, linear algebra and stochastic processes, making this textbook accessible to a wide audience."--Jacket. |
| Beschreibung: | 1 online resource (xvi, 569 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 1423753097 9781423753094 9786610758999 6610758999 1280758996 9781280758997 0191583006 9780191583001 9780198530237 0198530234 9780198530244 0198530242 |
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| 245 | 1 | 0 | |a Theory of neural information processing systems / |c A.C.C. Coolen, R. Kühn., P. Sollich. |
| 260 | |a Oxford : |b Oxford University Press, |c 2005. | ||
| 300 | |a 1 online resource (xvi, 569 pages) : |b illustrations | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "Theory of Neural Information Processing Systems provides an explicit, coherent, and up-to-date account of the modern theory of neural information processing systems. It has been carefully developed for graduate students from any quantitative discipline, including mathematics, computer science, physics, engineering, biology, and has been thoroughly class-tested by the authors over a period of some 8 years. Exercises are presented throughout the text and notes on historical background and further reading guide the students into the literature. All mathematical details are included and appendices provide further background material, including probability theory, linear algebra and stochastic processes, making this textbook accessible to a wide audience."--Jacket. | ||
| 546 | |a English. | ||
| 505 | 0 | 0 | |g Machine generated contents note: |g pt. I |t Introduction to neural networks -- |g 1. |t General introduction -- |g 2. |t Layered networks -- |g 3. |t Recurrent networks with binary neurons -- |g 4. |t Notes and suggestions for further reading -- |g pt. II |t Advanced neural networks -- |g 5. |t Competitive unsupervised learning processes -- |g 6. |t Bayesian techniques in supervised learning -- |g 7. |t Gaussian processes -- |g 8. |t Support vector machines for binary classification -- |g 9. |t Notes and suggestions for further reading -- |g pt. III |t Information theory and neural networks -- |g 10. |t Measuring information -- |g 11. |t Identification of entropy as an information measure -- |g 12. |t Building blocks of Shannon's information theory -- |g 13. |t Information theory and statistical inference -- |g 14. |t Applications to neural networks -- |g 15. |t Notes and suggestions for further reading -- |g pt. IV |t Macroscopic analysis of dynamics -- |g 16. |t Network operation : macroscopic dynamics -- |g 17. |t Dynamics of online learning in binary perceptions -- |g 18. |t Dynamics of online gradient descent learning -- |g 19. |t Notes and suggestions for further reading -- |g pt. V |t Equilibrium statistical mechanics of neural networks -- |g 20. |t Basics of equilibrium statistical mechanics -- |g 21. |t Network operation : equilibrium analysis -- |g 22. |t Gardner theory of task realizability -- |g 23. |t Notes and suggestions for further reading -- |g App. |t A Probability theory in a nutshell -- |g App. B |t Conditions for the central limit theorem to apply -- |g App. C |t Some simple summation identities -- |g App. D |t Gaussian integrals and probability distributions -- |g App. E |t Matrix identities -- |g App. F |t [delta]-distribution -- |g App. G |t Inequalities based on convexity -- |g App. H |t Metrics for parametrized probability distributions. |
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Neural networks (Computer science) |0 http://id.loc.gov/authorities/subjects/sh90001937 | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
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| 650 | 7 | |a Redes neuronales artificiales |2 embne | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 700 | 1 | |a Kühn, R. |q (Reimer), |d 1955- |1 https://id.oclc.org/worldcat/entity/E39PCjBwQyKfrmftcXMFfk8P73 |0 http://id.loc.gov/authorities/names/n2003001024 | |
| 700 | 1 | |a Sollich, P. |q (Peter) |1 https://id.oclc.org/worldcat/entity/E39PBJrwWVbqrJjD3brkKPFkDq |0 http://id.loc.gov/authorities/names/no2001050217 | |
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Datensatz im Suchindex
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| adam_text | |
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| author | Coolen, A. C. C. (Anthony C. C.), 1960- |
| author2 | Kühn, R. (Reimer), 1955- Sollich, P. (Peter) |
| author2_role | |
| author2_variant | r k rk p s ps |
| author_GND | http://id.loc.gov/authorities/names/nb2004312451 http://id.loc.gov/authorities/names/n2003001024 http://id.loc.gov/authorities/names/no2001050217 |
| author_facet | Coolen, A. C. C. (Anthony C. C.), 1960- Kühn, R. (Reimer), 1955- Sollich, P. (Peter) |
| author_role | |
| author_sort | Coolen, A. C. C. 1960- |
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| bvnumber | localFWS |
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| collection | ZDB-4-EBA |
| contents | Introduction to neural networks -- General introduction -- Layered networks -- Recurrent networks with binary neurons -- Notes and suggestions for further reading -- Advanced neural networks -- Competitive unsupervised learning processes -- Bayesian techniques in supervised learning -- Gaussian processes -- Support vector machines for binary classification -- Information theory and neural networks -- Measuring information -- Identification of entropy as an information measure -- Building blocks of Shannon's information theory -- Information theory and statistical inference -- Applications to neural networks -- Macroscopic analysis of dynamics -- Network operation : macroscopic dynamics -- Dynamics of online learning in binary perceptions -- Dynamics of online gradient descent learning -- Equilibrium statistical mechanics of neural networks -- Basics of equilibrium statistical mechanics -- Network operation : equilibrium analysis -- Gardner theory of task realizability -- A Probability theory in a nutshell -- Conditions for the central limit theorem to apply -- Some simple summation identities -- Gaussian integrals and probability distributions -- Matrix identities -- [delta]-distribution -- Inequalities based on convexity -- Metrics for parametrized probability distributions. |
| ctrlnum | (OCoLC)64238918 |
| dewey-full | 006.32 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.32 |
| dewey-search | 006.32 |
| dewey-sort | 16.32 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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Sollich.</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Oxford :</subfield><subfield code="b">Oxford University Press,</subfield><subfield code="c">2005.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xvi, 569 pages) :</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">"Theory of Neural Information Processing Systems provides an explicit, coherent, and up-to-date account of the modern theory of neural information processing systems. 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I</subfield><subfield code="t">Introduction to neural networks --</subfield><subfield code="g">1.</subfield><subfield code="t">General introduction --</subfield><subfield code="g">2.</subfield><subfield code="t">Layered networks --</subfield><subfield code="g">3.</subfield><subfield code="t">Recurrent networks with binary neurons --</subfield><subfield code="g">4.</subfield><subfield code="t">Notes and suggestions for further reading --</subfield><subfield code="g">pt. II</subfield><subfield code="t">Advanced neural networks --</subfield><subfield code="g">5.</subfield><subfield code="t">Competitive unsupervised learning processes --</subfield><subfield code="g">6.</subfield><subfield code="t">Bayesian techniques in supervised learning --</subfield><subfield code="g">7.</subfield><subfield code="t">Gaussian processes --</subfield><subfield code="g">8.</subfield><subfield code="t">Support vector machines for binary classification --</subfield><subfield code="g">9.</subfield><subfield code="t">Notes and suggestions for further reading --</subfield><subfield code="g">pt. III</subfield><subfield code="t">Information theory and neural networks --</subfield><subfield code="g">10.</subfield><subfield code="t">Measuring information --</subfield><subfield code="g">11.</subfield><subfield code="t">Identification of entropy as an information measure --</subfield><subfield code="g">12.</subfield><subfield code="t">Building blocks of Shannon's information theory --</subfield><subfield code="g">13.</subfield><subfield code="t">Information theory and statistical inference --</subfield><subfield code="g">14.</subfield><subfield code="t">Applications to neural networks --</subfield><subfield code="g">15.</subfield><subfield code="t">Notes and suggestions for further reading --</subfield><subfield code="g">pt. IV</subfield><subfield code="t">Macroscopic analysis of dynamics --</subfield><subfield code="g">16.</subfield><subfield code="t">Network operation : macroscopic dynamics --</subfield><subfield code="g">17.</subfield><subfield code="t">Dynamics of online learning in binary perceptions --</subfield><subfield code="g">18.</subfield><subfield code="t">Dynamics of online gradient descent learning --</subfield><subfield code="g">19.</subfield><subfield code="t">Notes and suggestions for further reading --</subfield><subfield code="g">pt. 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| id | ZDB-4-EBA-ocm64238918 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:15:50Z |
| institution | BVB |
| isbn | 1423753097 9781423753094 9786610758999 6610758999 1280758996 9781280758997 0191583006 9780191583001 9780198530237 0198530234 9780198530244 0198530242 |
| language | English |
| oclc_num | 64238918 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xvi, 569 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Oxford University Press, |
| record_format | marc |
| spelling | Coolen, A. C. C. (Anthony C. C.), 1960- https://id.oclc.org/worldcat/entity/E39PCjMyKVxfBwYJJbMCgkGq43 http://id.loc.gov/authorities/names/nb2004312451 Theory of neural information processing systems / A.C.C. Coolen, R. Kühn., P. Sollich. Oxford : Oxford University Press, 2005. 1 online resource (xvi, 569 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. "Theory of Neural Information Processing Systems provides an explicit, coherent, and up-to-date account of the modern theory of neural information processing systems. It has been carefully developed for graduate students from any quantitative discipline, including mathematics, computer science, physics, engineering, biology, and has been thoroughly class-tested by the authors over a period of some 8 years. Exercises are presented throughout the text and notes on historical background and further reading guide the students into the literature. All mathematical details are included and appendices provide further background material, including probability theory, linear algebra and stochastic processes, making this textbook accessible to a wide audience."--Jacket. English. Machine generated contents note: pt. I Introduction to neural networks -- 1. General introduction -- 2. Layered networks -- 3. Recurrent networks with binary neurons -- 4. Notes and suggestions for further reading -- pt. II Advanced neural networks -- 5. Competitive unsupervised learning processes -- 6. Bayesian techniques in supervised learning -- 7. Gaussian processes -- 8. Support vector machines for binary classification -- 9. Notes and suggestions for further reading -- pt. III Information theory and neural networks -- 10. Measuring information -- 11. Identification of entropy as an information measure -- 12. Building blocks of Shannon's information theory -- 13. Information theory and statistical inference -- 14. Applications to neural networks -- 15. Notes and suggestions for further reading -- pt. IV Macroscopic analysis of dynamics -- 16. Network operation : macroscopic dynamics -- 17. Dynamics of online learning in binary perceptions -- 18. Dynamics of online gradient descent learning -- 19. Notes and suggestions for further reading -- pt. V Equilibrium statistical mechanics of neural networks -- 20. Basics of equilibrium statistical mechanics -- 21. Network operation : equilibrium analysis -- 22. Gardner theory of task realizability -- 23. Notes and suggestions for further reading -- App. A Probability theory in a nutshell -- App. B Conditions for the central limit theorem to apply -- App. C Some simple summation identities -- App. D Gaussian integrals and probability distributions -- App. E Matrix identities -- App. F [delta]-distribution -- App. G Inequalities based on convexity -- App. H Metrics for parametrized probability distributions. Print version record. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Réseaux neuronaux (Informatique) COMPUTERS Neural Networks. bisacsh Redes neuronales artificiales embne Neural networks (Computer science) fast Kühn, R. (Reimer), 1955- https://id.oclc.org/worldcat/entity/E39PCjBwQyKfrmftcXMFfk8P73 http://id.loc.gov/authorities/names/n2003001024 Sollich, P. (Peter) https://id.oclc.org/worldcat/entity/E39PBJrwWVbqrJjD3brkKPFkDq http://id.loc.gov/authorities/names/no2001050217 Print version: Coolen, A.C.C. (Anthony C.C.), 1960- Theory of neural information processing systems. Oxford : Oxford University Press, 2005 0198530234 0198530242 (DLC) 2006295078 (OCoLC)61301413 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=149338 Volltext |
| spellingShingle | Coolen, A. C. C. (Anthony C. C.), 1960- Theory of neural information processing systems / Introduction to neural networks -- General introduction -- Layered networks -- Recurrent networks with binary neurons -- Notes and suggestions for further reading -- Advanced neural networks -- Competitive unsupervised learning processes -- Bayesian techniques in supervised learning -- Gaussian processes -- Support vector machines for binary classification -- Information theory and neural networks -- Measuring information -- Identification of entropy as an information measure -- Building blocks of Shannon's information theory -- Information theory and statistical inference -- Applications to neural networks -- Macroscopic analysis of dynamics -- Network operation : macroscopic dynamics -- Dynamics of online learning in binary perceptions -- Dynamics of online gradient descent learning -- Equilibrium statistical mechanics of neural networks -- Basics of equilibrium statistical mechanics -- Network operation : equilibrium analysis -- Gardner theory of task realizability -- A Probability theory in a nutshell -- Conditions for the central limit theorem to apply -- Some simple summation identities -- Gaussian integrals and probability distributions -- Matrix identities -- [delta]-distribution -- Inequalities based on convexity -- Metrics for parametrized probability distributions. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Réseaux neuronaux (Informatique) COMPUTERS Neural Networks. bisacsh Redes neuronales artificiales embne Neural networks (Computer science) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 |
| title | Theory of neural information processing systems / |
| title_alt | Introduction to neural networks -- General introduction -- Layered networks -- Recurrent networks with binary neurons -- Notes and suggestions for further reading -- Advanced neural networks -- Competitive unsupervised learning processes -- Bayesian techniques in supervised learning -- Gaussian processes -- Support vector machines for binary classification -- Information theory and neural networks -- Measuring information -- Identification of entropy as an information measure -- Building blocks of Shannon's information theory -- Information theory and statistical inference -- Applications to neural networks -- Macroscopic analysis of dynamics -- Network operation : macroscopic dynamics -- Dynamics of online learning in binary perceptions -- Dynamics of online gradient descent learning -- Equilibrium statistical mechanics of neural networks -- Basics of equilibrium statistical mechanics -- Network operation : equilibrium analysis -- Gardner theory of task realizability -- A Probability theory in a nutshell -- Conditions for the central limit theorem to apply -- Some simple summation identities -- Gaussian integrals and probability distributions -- Matrix identities -- [delta]-distribution -- Inequalities based on convexity -- Metrics for parametrized probability distributions. |
| title_auth | Theory of neural information processing systems / |
| title_exact_search | Theory of neural information processing systems / |
| title_full | Theory of neural information processing systems / A.C.C. Coolen, R. Kühn., P. Sollich. |
| title_fullStr | Theory of neural information processing systems / A.C.C. Coolen, R. Kühn., P. Sollich. |
| title_full_unstemmed | Theory of neural information processing systems / A.C.C. Coolen, R. Kühn., P. Sollich. |
| title_short | Theory of neural information processing systems / |
| title_sort | theory of neural information processing systems |
| topic | Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Réseaux neuronaux (Informatique) COMPUTERS Neural Networks. bisacsh Redes neuronales artificiales embne Neural networks (Computer science) fast |
| topic_facet | Neural networks (Computer science) Réseaux neuronaux (Informatique) COMPUTERS Neural Networks. Redes neuronales artificiales |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=149338 |
| work_keys_str_mv | AT coolenacc theoryofneuralinformationprocessingsystems AT kuhnr theoryofneuralinformationprocessingsystems AT sollichp theoryofneuralinformationprocessingsystems |