Emerging paradigms in machine learning:
<p>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 a...
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2013
|
| Schriftenreihe: | Smart innovation, systems and technologies
13 |
| Schlagworte: | |
| Online-Zugang: | BTU01 FHA01 FHI01 FHN01 FHR01 FKE01 FWS01 UBY01 Volltext |
| Zusammenfassung: | <p>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. </p> |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource (XXII, 498 p. 167 illus) |
| ISBN: | 9783642286995 |
| DOI: | 10.1007/978-3-642-28699-5 |
Internformat
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Datensatz im Suchindex
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| id | DE-604.BV040800328 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T16:14:46Z |
| institution | BVB |
| isbn | 9783642286995 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025780446 |
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| physical | 1 Online-Ressource (XXII, 498 p. 167 illus) |
| psigel | ZDB-2-ENG |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| series | Smart innovation, systems and technologies |
| series2 | Smart innovation, systems and technologies |
| spellingShingle | Emerging paradigms in machine learning Smart innovation, systems and technologies Ingenieurwissenschaften Künstliche Intelligenz Engineering Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4143413-4 |
| title | Emerging paradigms in machine learning |
| title_auth | Emerging paradigms in machine learning |
| title_exact_search | Emerging paradigms in machine learning |
| title_full | Emerging paradigms in machine learning Sheela Ramanna ..., eds. |
| title_fullStr | Emerging paradigms in machine learning Sheela Ramanna ..., eds. |
| title_full_unstemmed | Emerging paradigms in machine learning Sheela Ramanna ..., eds. |
| title_short | Emerging paradigms in machine learning |
| title_sort | emerging paradigms in machine learning |
| topic | Ingenieurwissenschaften Künstliche Intelligenz Engineering Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Ingenieurwissenschaften Künstliche Intelligenz Engineering Artificial intelligence Maschinelles Lernen Aufsatzsammlung |
| url | https://doi.org/10.1007/978-3-642-28699-5 |
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