Emerging Paradigms in Machine Learning (Record no. 55734)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03127nam a22004935i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-642-28699-5 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421111844.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 120730s2013 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783642286995 |
| -- | 978-3-642-28699-5 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 245 10 - TITLE STATEMENT | |
| Title | Emerging Paradigms in Machine Learning |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XXII, 498 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Smart Innovation, Systems and Technologies, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | From the content: Emerging Paradigms in Machine Learning: An Introduction -- Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization -- Optimised information abstraction in granular Min/Max clustering -- Mining Incomplete Data-A Rough Set Approach -- Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book. . |
| 700 1# - AUTHOR 2 | |
| Author 2 | Ramanna, Sheela. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Jain, Lakhmi C. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Howlett, Robert J. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-642-28699-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Berlin, Heidelberg : |
| -- | Springer Berlin Heidelberg : |
| -- | Imprint: Springer, |
| -- | 2013. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence (incl. Robotics). |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2190-3018 ; |
| 912 ## - | |
| -- | ZDB-2-ENG |
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