Machine Learning for Data Streams : with Practical Examples in MOA /

Machine Learning for Data Streams : with Practical Examples in MOA /

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.

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Bibliographic Details
Main Authors: Bifet, Albert (Author), Pfahringer, Bernhard (Author), Holmes, Geoffrey (Author), Gavaldà, Ricard, 1964- (Author)
Format: Electronic eBook
Language:English
Published: London, England : The MIT Press, [2017]
Series:Book collections on Project MUSE.
Subjects:
Neural networks (Computer science)
Machine learning.
Database management.
COMPUTERS / Artificial Intelligence / General.
Apprentissage automatique.
Reseaux neuronaux (Informatique)
Bases de donnees > Gestion.
Electronic books.
Online Access:Full text available:
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Table of Contents:
  • Intro; Contents; List of Figures; List of Tables; Preface; I INTRODUCTION; 1 Introduction; 2 Big Data Stream Mining; 3 Hands-on Introduction to MOA; II STREAM MINING; 4 Streams and Sketches; 5 Dealing with Change; 6 Classification; 7 Ensemble Methods; 8 Regression; 9 Clustering; 10 Frequent Pattern Mining; III THE MOA SOFTWARE; 11 Introduction to MOA and Its Ecosystem; 12 The Graphical User Interface; 13 Using the Command Line; 14 Using the API; 15 Developing New Methods in MOA; Bibliography; Index

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