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Mastering Java Machine Learning

You're reading from   Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785880513
Length 556 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
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Table of Contents (13) Chapters Close

Preface 1. Machine Learning Review FREE CHAPTER 2. Practical Approach to Real-World Supervised Learning 3. Unsupervised Machine Learning Techniques 4. Semi-Supervised and Active Learning 5. Real-Time Stream Machine Learning 6. Probabilistic Graph Modeling 7. Deep Learning 8. Text Mining and Natural Language Processing 9. Big Data Machine Learning – The Final Frontier A. Linear Algebra B. Probability Index

Graph concepts


Next, we will briefly revisit the concepts from graph theory and some of the definitions that we will use in this chapter.

Graph structure and properties

A graph is defined as a data structure containing nodes and edges connecting these nodes. In the context of this chapter, the random variables are represented as nodes, and edges show connections between the random variables.

Formally, if X = {X1, X2,….Xk} where X1, X2,….Xk are random variables representing the nodes, then there can either be a directed edge belonging to the set e, for example, between the nodes given by or an undirected edge , and the graph is defined as a data structure . A graph is said to be a directed graph when every edge in the set e between nodes from set X is directed and similarly an undirected graph is one where every edge between the nodes is undirected as shown in Figure 1. Also, if there is a graph that has both directed and undirected edges, the notation of represents an edge that may be...

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