Book Image

Machine Learning in Java - Second Edition

By : AshishSingh Bhatia, Bostjan Kaluza
Book Image

Machine Learning in Java - Second Edition

By: AshishSingh Bhatia, Bostjan Kaluza

Overview of this book

As the amount of data in the world 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. Machine Learning in Java will provide you with the techniques and tools you need. 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. The code in this book works for JDK 8 and above, the code is tested on JDK 11. 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 have explored related web resources and technologies that will help you take your learning to the next level. 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.
Table of Contents (13 chapters)

Suspicious pattern detection

The first approach involves a behavior library that encodes negative patterns, shown as red minus signs in the following diagram, and recognizes that observed behavior corresponds to identifying a match in the library. If a new pattern can be matched against negative patterns, then it is considered suspicious:

For example, when you visit a doctor, he/she inspects various health symptoms (body temperature, pain levels, affected areas, and so on) and matches the symptoms to a known disease. In machine learning terms, the doctor collects attributes and performs classifications.

An advantage of this approach is that we immediately know what is wrong; for example, assuming that we know the disease, we can select an appropriate treatment procedure.

A major disadvantage of this approach is that it can only detect suspicious patterns that are known in advance...