Bayesian speech and language processing / (Record no. 84207)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02717nam a2200373 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | CR9781107295360 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730160759.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 130705s2015||||enk o ||1 0|eng|d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781107295360 (ebook) |
| 082 00 - CLASSIFICATION NUMBER | |
| Call Number | 410.1/51 |
| 100 1# - AUTHOR NAME | |
| Author | Watanabe, Shinji |
| 245 10 - TITLE STATEMENT | |
| Title | Bayesian speech and language processing / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource (xxi, 424 pages) : |
| 500 ## - GENERAL NOTE | |
| Remark 1 | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems in speech and language processing. A range of statistical models is detailed, from hidden Markov models to Gaussian mixture models, n-gram models and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calculations, useful notations, formulas, and rules. The authors address the difficulties of straightforward applications and provide detailed examples and case studies to demonstrate how you can successfully use practical Bayesian inference methods to improve the performance of information systems. This is an invaluable resource for students, researchers, and industry practitioners working in machine learning, signal processing, and speech and language processing. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Machine generated contents note: Part I. General Discussion: 1. Introduction; 2. Bayesian approach; 3. Statistical models in speech and language processing; Part II. Approximate Inference: 4. Maximum a posteriori approximation; 5. Evidence approximation; 6. Asymptotic approximation; 7. Variational Bayes; 8. Markov chain Monte Carlo. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Study and teaching |
| -- | Statistical method. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Chien, Jen-Tzung, |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1017/CBO9781107295360 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge : |
| -- | Cambridge University Press, |
| -- | 2015. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Language and languages |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Bayesian statistical decision theory. |
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