Mahout in Action (Paperback)

Sean Owen, Robin Anil, Ted Dunning, Ellen Friedman

  • 出版商: Manning
  • 出版日期: 2011-10-17
  • 售價: $1,440
  • 貴賓價: 9.5$1,368
  • 語言: 英文
  • 頁數: 416
  • 裝訂: Paperback
  • ISBN: 1935182684
  • ISBN-13: 9781935182689
  • 相關翻譯: Mahout實戰 (簡中版)

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Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook.

About the Technology

A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others.

About this Book

This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework.

This book is written for developers familiar with Java - no prior experience with Mahout is assumed.

What's Inside
  • Use group data to make individual recommendations
  • Find logical clusters within your data
  • Filter and refine with on-the-fly classification
  • Free audio and video extras
Table of Contents
  1. Meet Apache Mahout
  2. Introducing recommenders
  3. Representing recommender data
  4. Making recommendations
  5. Taking recommenders to production
  6. Distributing recommendation computations
  7. Introduction to clustering
  8. Representing data
  9. Clustering algorithms in Mahout
  10. Evaluating and improving clustering quality
  11. Taking clustering to production
  12. Real-world applications of clustering
  13. Introduction to classification
  14. Training a classifier
  15. Evaluating and tuning a classifier
  16. Deploying a classifier
  17. Case study: Shop It To Me