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Machine Learning in Java

You're reading from   Machine Learning in Java Helpful techniques to design, build, and deploy powerful machine learning applications in Java

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788474399
Length 300 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Bostjan Kaluza Bostjan Kaluza
Author Profile Icon Bostjan Kaluza
Bostjan Kaluza
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Table of Contents (13) Chapters Close

Preface 1. Applied Machine Learning Quick Start FREE CHAPTER 2. Java Libraries and Platforms for Machine Learning 3. Basic Algorithms - Classification, Regression, and Clustering 4. Customer Relationship Prediction with Ensembles 5. Affinity Analysis 6. Recommendation Engines with Apache Mahout 7. Fraud and Anomaly Detection 8. Image Recognition with Deeplearning4j 9. Activity Recognition with Mobile Phone Sensors 10. Text Mining with Mallet - Topic Modeling and Spam Detection 11. What Is Next? 12. Other Books You May Enjoy

Anomalous pattern detection

The second approach uses the pattern library in an inverse fashion, meaning that the library encodes only the positive patterns, which are marked with green plus signs in the following diagram. When an observed behavior (the blue circle) cannot be matched against the library, it is considered anomalous:

This approach requires us to model only what we have seen in the past, that is, normal patterns. If we return to the doctor example, the main reason that we visited the doctor in the first place was because we did not feel well. Our perceived state of feelings (for example, a headache and sore skin) did not match our usual feelings, and therefore, we decided to seek a doctor. We don't know which disease caused this state, nor do we know the treatment, but we were able to observe that it doesn't match the usual state.

A major advantage of this...

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