Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Chapter 4. Semi-Supervised and Active Learning

In Chapter 2, Practical Approach to Real-World Supervised Learning and Chapter 3, Unsupervised Machine Learning Techniques, we discussed two major groups of machine learning techniques which apply to opposite situations when it comes to the availability of labeled data—one where all target values are known and the other where none are. In contrast, the techniques in this chapter address the situation when we must analyze and learn from data that is a mix of a small portion with labels and a large number of unlabeled instances.

In speech and image recognition, a vast quantity of data is available, and in various forms. However, the cost of labeling or classifying all that data is costly and therefore, in practice, the proportion of speech or images that are classified to those that are not classified is very small. Similarly, in web text or document classification, there are an enormous number of documents on the World Wide Web but classifying...