The case study in this chapter consists of several experiments that illustrate different methods of stream-based machine learning. A well-studied dataset was chosen as the stream data source and supervised tree based methods such as Naïve Bayes, Hoeffding Tree, as well as ensemble methods, were used. Among unsupervised methods, clustering algorithms used include k-Means, DBSCAN, CluStream, and CluTree. Outlier detection techniques include MCOD and SimpleCOD, among others. We also show results from classification experiments that demonstrate handling concept drift. The ADWIN algorithm for calculating statistics in a sliding window, as described earlier in this chapter, is employed in several algorithms used in the classification experiments.
Mastering Java Machine Learning
By :
Mastering Java Machine Learning
By:
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
Free Chapter
Machine Learning Review
Practical Approach to Real-World Supervised Learning
Unsupervised Machine Learning Techniques
Semi-Supervised and Active Learning
Real-Time Stream Machine Learning
Probabilistic Graph Modeling
Deep Learning
Text Mining and Natural Language Processing
Big Data Machine Learning – The Final Frontier
Linear Algebra
Probability
Index
Customer Reviews