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Machine learning : a Bayesian and optimization perspective /
"This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches--which are based on optimization techniques--together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models"...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam [Netherlands] :
Academic Press,
2015.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
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| 001 | EBOOKCENTRAL_ocn908071759 | ||
| 003 | OCoLC | ||
| 005 | 20240329122006.0 | ||
| 006 | m o d | ||
| 007 | cr cn||||||||| | ||
| 008 | 150415t20152015ne a ob 001 0 eng d | ||
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| 019 | |a 1014396548 |a 1014432633 |a 1014475963 |a 1014486421 | ||
| 020 | |a 9780128017227 |q (e-book) | ||
| 020 | |a 0128017228 |q (e-book) | ||
| 020 | |z 9780128015223 | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Theodoridis, Sergios, |d 1951- |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjxcjmqG9c6pj4TqgXMGjK | |
| 245 | 1 | 0 | |a Machine learning : |b a Bayesian and optimization perspective / |c Sergios Theodoridis. |
| 264 | 1 | |a Amsterdam [Netherlands] : |b Academic Press, |c 2015. | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource (1,075 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ebrary, viewed April 15, 2015). | |
| 505 | 0 | |a Probability and stochastic processes -- Learning in parametric modeling: basic concepts and directions -- Mean-square error linear estimation -- Stochastic gradient descent: the LMS algorithm -- The least-squares family -- Classification: a tour of the classics -- Parameter learning: a convex analytic path -- Sparsity-aware learning: concepts and theoretical foundations -- Sparcity-aware learning: algorithms and applications -- Learning in reproducing Kernel Hilbert spaces -- Bayesian learning: inference and the EM alogrithm -- Bayesian learning: approximate inference and nonparametric models -- Monte Carlo methods -- Probabilistic graphical models: Part I -- Probabilistic graphical models: Part II -- Particle filtering -- Neural networks and deep learning -- Dimensionality reduction -- Appendix A LInear algebra -- Appendix B Probability theory and statistics -- Appendix C Hints on constrained optimization. | |
| 520 | |a "This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches--which are based on optimization techniques--together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models"--Publisher's website | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Mathematical optimization |2 fast | |
| 758 | |i has work: |a Machine learning (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGYgkMR6v7xQVdMM9p3hHC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Theodoridis, Sergios. |t Machine learning : a Bayesian and optimization perspective. |d Amsterdam, [Netherlands] : Academic Press, ©2015 |h xxi, 1050 pages |z 9780128015223 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=2007481 |z Texto completo |
| 936 | |a BATCHLOAD | ||
| 938 | |a ebrary |b EBRY |n ebr11040166 | ||
| 938 | |a Internet Archive |b INAR |n machinelearningb0000theo | ||
| 994 | |a 92 |b IZTAP | ||