Machine Learning for Data Streams : with Practical Examples in MOA / Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer.

By: Contributor(s): Material type: TextTextSeries: Adaptive computation and machine learning | Book collections on Project MUSEPublisher: London, England : The MIT Press, [2017]Manufacturer: Baltimore, Md. : Project MUSE, 2018Copyright date: ©[2017]Description: 1 online resource: illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780262346047
Subject(s): Genre/Form: Online resources:
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
Summary: 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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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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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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