Foundations of machine learning / (Record no. 1887)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03514cam a2200313 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MIUC |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20211026131114.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 211026s2018 maua b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780262039406 |
| Qualifying information | (hardcover : alk. paper) |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | DLC |
| Language of cataloging | eng |
| Transcribing agency | DLC |
| Description conventions | rda |
| Modifying agency | DLC |
| -- | MIUC |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mohri, Mehryar |
| Relator term | author |
| 9 (RLIN) | 5438 |
| 952 ## - Items | |
| Itemnumber | 2252 |
| 245 10 - TITLE STATEMENT | |
| Title | Foundations of machine learning / |
| Statement of responsibility, etc. | Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | Second edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Cambridge, Massachusetts ; |
| -- | London, England : |
| Name of producer, publisher, distributor, manufacturer | The MIT Press, |
| Date of production, publication, distribution, manufacture, or copyright notice | [2018]. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xv, 486 pages : |
| Other physical details | illustrations (some colour) ; |
| Dimensions | 24 cm. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 490 0# - SERIES STATEMENT | |
| Series statement | Adaptive computation and machine learning |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes bibliographical references (pages 461-474) and index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1. Introduction -- <br/>2. The PAC Learning Framework -- <br/>3. Rademacher Complexity and VC-Dimension -- <br/>4. Model Selection -- <br/>5. Support Vector Machines -- <br/>6. Kernel Methods -- <br/>7. Boosting -- <br/>8. On-line Learning -- <br/>9. Multi-Class Classification -- <br/>10. Ranking -- <br/>11. Regression -- <br/>12. Maximum Entropy -- <br/>13. Conditional Maximum Entropy Models -- <br/>14. Algorithmic Stability -- <br/>15. Dimensionality Reduction -- <br/>16. Learning Automata and Language -- <br/>17. Reinforcement Learning -- <br/>Conclusion -- <br/>A. Linear Algebra Review -- <br/>B. Convex Optimization -- <br/>C. Probability Review -- <br/>D. Concentration Inequalities -- <br/>E. Notions of Information Theory<br/>F. Notation.<br/><br/> |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.<br/><br/>This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.<br/><br/>Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review.<br/><br/>This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning |
| 9 (RLIN) | 5439 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computer algorithms |
| 9 (RLIN) | 5440 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Rostamizadeh, Afshin |
| Relator term | author |
| 9 (RLIN) | 5441 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Talwalkar, Ameet |
| Relator term | author |
| 9 (RLIN) | 5442 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Date last seen | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Marbella International University Centre | Marbella International University Centre | Library | 20/10/2021 | 1 | 69.59 | 006.31 MOH fou | 20/10/2021 | 69.59 | 20/10/2021 | Books |