Bayesian reasoning and machine learning / (Record no. 241302)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04126cam a2200277 a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220518154613.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 110822s2011 enka b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781107439955 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 004.85 |
| Item number | BAR |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Barber, David, |
| 9 (RLIN) | 2338 |
| 245 10 - TITLE STATEMENT | |
| Title | Bayesian reasoning and machine learning / |
| Statement of responsibility, etc. | David Barber. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Cambridge ; |
| -- | New York : |
| Name of publisher, distributor, etc. | Cambridge University Press, |
| Date of publication, distribution, etc. | 2012. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxiv, 697 p. : |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Machine generated contents note: Preface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discrete-state Markov models; 24. Continuous-state Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | "Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online"-- |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | "Vast amounts of data present amajor challenge to all thoseworking in computer science, and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for final-year undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning, and rucially enables students to construct their own models for real-world problems by teaching them what lies behind the methods. Numerous examples and exercises are included in the text. Comprehensive resources for students and instructors are available online"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning. |
| 9 (RLIN) | 55 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Bayesian statistical decision theory. |
| 9 (RLIN) | 2339 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | COMPUTERS / Computer Vision & Pattern Recognition. |
| 9 (RLIN) | 2340 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
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| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
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| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://www.loc.gov/catdir/enhancements/fy1117/2011035553-d.html">http://www.loc.gov/catdir/enhancements/fy1117/2011035553-d.html</a> |
| 856 41 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://www.loc.gov/catdir/enhancements/fy1117/2011035553-t.html">http://www.loc.gov/catdir/enhancements/fy1117/2011035553-t.html</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
No items available.