Machine Learning for Data Streams: with Practical Examples in MOA (Hardback)
  • Machine Learning for Data Streams: with Practical Examples in MOA (Hardback)
zoom

Machine Learning for Data Streams: with Practical Examples in MOA (Hardback)

(author), (author), (author), (author), (editor)
£43.00
Hardback 288 Pages / Published: 02/03/2018
  • We can order this

Usually dispatched within 1 week

  • This item has been added to your basket
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. Today many information sources-including sensor networks, financial markets, social networks, and healthcare monitoring-are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

Publisher: MIT Press Ltd
ISBN: 9780262037792
Number of pages: 288
Dimensions: 229 x 178 x 24 mm

You may also be interested in...

Introduction to Machine Learning
Added to basket
Machine Learning
Added to basket
£39.99
Paperback
Machine Learning
Added to basket
£42.50
Paperback
Machine Learning
Added to basket
£52.99
Mixed media product
Reinforcement Learning
Added to basket
Superintelligence
Added to basket
£18.99
Hardback
Machine Learning
Added to basket
Understanding Machine Learning
Added to basket
Thoughtful Machine Learning
Added to basket
Probabilistic Graphical Models
Added to basket
The Elements of Statistical Learning
Added to basket
Machine Learning for Hackers
Added to basket
Web Data Mining
Added to basket
£54.99
Hardback
Bayesian Reasoning and Machine Learning
Added to basket

Reviews

Please sign in to write a review

Your review has been submitted successfully.