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)

Topic modeling for BBC News

As discussed earlier, the goal of topic modeling is to identify patterns in a text corpus that correspond to document topics. In this example, we will use a dataset originating from BBC News. This dataset is one of the standard benchmarks in machine-learning research, and is available for non-commercial and research purposes.

The goal is to build a classifier that is able to assign a topic to an uncategorized document.

BBC dataset

In 2006, Greene and Cunningham collected the BBC dataset to study a particular document—Clustering challenge using support vector machines. The dataset consists of 2,225 documents from the BBC News website from 2004 to 2005, corresponding to the stories collected...