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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 (13 chapters)
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10
A. Linear Algebra
12
Index

Specialized networks


In this section, we will cover some basic specialized probabilistic graph models that are very useful in different machine learning applications.

Tree augmented network

In Chapter 2, Practical Approach to Real-World Supervised Learning, we discussed the Naïve Bayes network, which makes the simplified assumption that all variables are independent of each other and only have dependency on the target or the class variable. This is the simplest Bayesian network derived or assumed from the dataset. As we saw in the previous sections, learning complex structures and parameters in Bayesian networks can be difficult or sometimes intractable. The tree augmented network or TAN (References [9]) can be considered somewhere in the middle, introducing constraints on how the trees are connected. TAN puts a constraint on features or variable relationships. A feature can have only one other feature as parent in addition to the target variable, as illustrated in the following figure:

Figure...

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