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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
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Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
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Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
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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