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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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Toc

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 Graph structure and properties or an undirected edge Graph structure and properties, and the graph is defined as a data structure Graph structure and properties. 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 Graph structure and properties represents an edge that may be directed or undirected.

Graph structure and properties

Figure 1. Directed, undirected, and partially-directed graphs

If a directed edge Graph structure and properties exists in the graph, the node Xi is called the parent node and the node Xj is called the child node.

In the case of an undirected graph, if there is an edge Xi – Xj, the nodes Xi and Xj are said to be neighbors.

The set of parents of node X in a directed graph is called the boundary of the node X and similarly, adjacent nodes in an undirected graph form each other's boundary. The degree of the node X is the number of edges it participates in. The indegree of the node X is the number of edges in the directed graph that have a relationship to node X such that the edge is between node Y and node X and X → Y. The degree of the graph is the maximal degree of the node in that graph.

Subgraphs and cliques

A subgraph is part of the graph that represents some of the nodes from the entire set. A clique is a subset of vertices in an undirected graph such that every two distinct vertices are adjacent.

Path, trail, and cycles

If there are variables X1, X2, …. Xk in the graph K = (X, E), it forms a path if, for every i = 1, 2 ... k – 1, we have either Path, trail, and cycles or Xi –Xj; that is, there is either a directed edge or an undirected edge between the variables—recall this can be depicted as Xi ? Xj. A directed path has at least one directed edge: Path, trail, and cycles.

If there are variables X1, X2, …. Xk in the graph K = (X, E) it forms a trail if for every i = 1, 2 ... k – 1, we have either Path, trail, and cycles.

A graph is called a connected graph, if for every Xi, ….Xj there is a trail between Xi and Xj.

In a graph K = (X, e), if there is a directed path between nodes X and Y, X is called the ancestor of Y and Y is called the descendant of X.

If a graph K has a directed path X1, X2, …. Xk where X1 ? Xk, the path is called a cycle. Conversely, a graph with no cycles is called an acyclic graph.

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