Data Mining: Foundations and Intelligent Paradigms
VOLUME 2: Statistical, Bayesian, Time Series and other Theoretical Aspects
(Sprache: Englisch)
Data mining is a rapidly growing research area in computer science and statistics. Volume 2 of this three-volume series covers theoretical aspects of the subject, including statistical, Bayesian, time-series and others relevant to health informatics.
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Data mining is a rapidly growing research area in computer science and statistics. Volume 2 of this three-volume series covers theoretical aspects of the subject, including statistical, Bayesian, time-series and others relevant to health informatics.
Klappentext zu „Data Mining: Foundations and Intelligent Paradigms “
There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled "DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis" we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.
Inhaltsverzeichnis zu „Data Mining: Foundations and Intelligent Paradigms “
From the content: Data Mining with Multilayer Perceptrons and Support Vector Machines.- Regulatory Networks under Ellipsoidal Uncertainty - Data Analysis and Prediction by Optimization Theory and Dynamical Systems.- A Visual Environment for Designing and Running Data Mining Workflows in the Knowledge Grid.- Formal framework for the Study of Algorithmic Properties of Objective Interestingness Measures.- Nonnegative Matrix Factorization: Models, Algorithms and Applications.- Visual Data Mining and Discovery with Binarized Vectors.
Bibliographische Angaben
- 2014, 2012, XIV, 250 Seiten, Maße: 15,6 x 23,6 cm, Kartoniert (TB), Englisch
- Herausgegeben: Dawn E. Holmes, Lakhmi C Jain
- Verlag: Springer, Berlin
- ISBN-10: 3642434290
- ISBN-13: 9783642434297
Sprache:
Englisch
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