Book Image

Machine Learning in Java

By : Bostjan Kaluza
Book Image

Machine Learning in Java

By: Bostjan Kaluza

Overview of this book

<p>As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.</p> <p>Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering.</p> <p>Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will explore related web resources and technologies that will help you take your learning to the next level.</p> <p>By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.</p>
Table of Contents (19 chapters)
Machine Learning in Java
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
References
Index

Anomalous pattern detection


The second approach uses the pattern library in an inverse fashion, meaning that the library encodes only positive patterns marked with green plus signs in the following image. When an observed behavior (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 we visited the doctor in the first place was because we did not feel fine. Our perceived state of feelings (for example, headache, sore skin) did not match our usual feelings, therefore, we decided to seek 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 approach is that it does not require us to say anything about non-normal patterns; hence, it is appropriate for modeling known-unknowns and unknown-unknowns. On...