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

Introducing activity recognition


Activity recognition is an underpinning step in behavior analysis, addressing healthy lifestyle, fitness tracking, remote assistance, security applications, elderly care, and so on. Activity recognition transforms low-level sensor data from sensors, such as accelerometer, gyroscope, pressure sensor, and GPS location, to a higher-level description of behavior primitives. In most cases, these are basic activities, for example, walking, sitting, lying, jumping, and so on, as shown in the following image, or they could be more complex behaviors, such as going to work, preparing breakfast, shopping, and so on:

In this chapter, we will discuss how to add the activity recognition functionality into a mobile application. We will first look at what does an activity recognition problem looks like, what kind of data do we need to collect, what are the main challenges are, and how to address them?

Later, we will follow an example to see how to actually implement activity...