Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	März 2017
Genre:	EDV / Informatik
	A / Artificial Intelligence / Automated Pattern Recognition / computer science / Data Mining / Data Mining and Knowledge Discovery / Expert systems / knowledge-based systems / Mathematical & statistical software / Mathematical and statistical software / Mathematische und statistische Software / Mustererkennung / pattern recognition / Probability and statistics / recommender systems / Reinforcement Learning / sequential pattern analysis / statistical language learning / statistical maching learning / Statistics / Statistics and Computing / Statistics and Computing/Statistics Programs / Text Mining / Wahrscheinlichkeitsrechnung und Statistik / Wissensbasierte Systeme, Expertensysteme mehr...
ISBN:	9781489976857
EAN-Code:	9781489976857
Verlag:	Springer EN
Einband:	Set (Buch und div.)
Sprache:	English
Dimensionen:	H 254 mm / B 178 mm / D
Gewicht:	3081 gr
Seiten:	1335
Illustration:	XVII, 1335 p. 263 illus., 83 illus. in color. In 2 volumes, not available separately., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining.  A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others.  Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.