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Mastering Java Machine Learning

You're reading from   Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785880513
Length 556 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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 Kamath Kamath
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Kamath
Krishna Choppella Krishna Choppella
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Krishna Choppella
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Table of Contents (20) Chapters Close

Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Machine Learning Review 2. Practical Approach to Real-World Supervised Learning FREE CHAPTER 3. Unsupervised Machine Learning Techniques 4. Semi-Supervised and Active Learning 5. Real-Time Stream Machine Learning 6. Probabilistic Graph Modeling 7. Deep Learning 8. Text Mining and Natural Language Processing 9. Big Data Machine Learning – The Final Frontier Linear Algebra Probability Index

Outlier or anomaly detection


Grubbs, in 1969, offers the definition, "An outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs".

Hawkins, in 1980, defined outliers or anomaly as "an observation which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism".

Barnett and Lewis, 1994, defined it as "an observation (or subset of observations) which appears to be inconsistent with the remainder of that set of data".

Outlier algorithms

Outlier detection techniques are classified based on different approaches to what it means to be an outlier. Each approach defines outliers in terms of some property that sets apart some objects from others in the dataset:

  • Statistical-based: This is improbable according to a chosen distribution

  • Distance-based: This is isolated from neighbors according to chosen distance measure and fraction of neighbors within threshold distance

  • Density-based...

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