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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
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Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: 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)
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Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Summary


After a tour of supervised and unsupervised machine learning techniques and their application to real-world datasets in the previous chapters, this chapter introduces the concepts, techniques, and tools of Semi-Supervised Learning (SSL) and Active Learning (AL).

In SSL, we are given a few labeled examples and many unlabeled ones—the goal is either to simply train on the labeled ones in order to classify the unlabeled ones (transductive SSL), or use the unlabeled and labeled examples to train models to correctly classify new, unseen data (inductive SSL). All techniques in SSL are based on one or more of the assumptions related to semi-supervised smoothness, cluster togetherness, and manifold togetherness.

Different SSL techniques are applicable to different situations. The simple self-training SSL is straightforward and works with most supervised learning algorithms; when the data is from more than just one domain, the co-training SSL is a suitable method. When the cluster togetherness...

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