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Hands-On Artificial Intelligence with Java for Beginners

Hands-On Artificial Intelligence with Java for Beginners

By : Joshi
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Hands-On Artificial Intelligence with Java for Beginners

Hands-On Artificial Intelligence with Java for Beginners

1.3 (3)
By: Joshi
Buy this Book

Overview of this book

Artificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data. AI can be used for automating systems or processes to carry out complex tasks and functions in order to achieve optimal performance and productivity. Hands-On Artificial Intelligence with Java for Beginners begins by introducing you to AI concepts and algorithms. You will learn about various Java-based libraries and frameworks that can be used in implementing AI to build smart applications. In addition to this, the book teaches you how to implement easy to complex AI tasks, such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation, all with a practical approach. By the end of this book, you will not only have a solid grasp of AI concepts, but you'll also be able to build your own smart applications for multiple domains.
Table of Contents (9 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
Introduction to Artificial Intelligence and Java
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Introduction to Artificial Intelligence and Java
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What is machine learning?
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Differences between classification and regression
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Installing JDK and JRE
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Setting up the NetBeans IDE
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Importing Java libraries and exporting code in projects as a JAR file
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Summary
2
Exploring Search Algorithms
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Exploring Search Algorithms
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An introduction to searching
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Implementing Dijkstra's search
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Understanding the notion of heuristics
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A brief introduction to the A* algorithm
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Implementing an A* algorithm
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Summary
3
AI Games and the Rule-Based System
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AI Games and the Rule-Based System
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Introducing the min-max algorithm
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Implementing an example min-max algorithm
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Installing Prolog
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An introduction to rule-based systems with Prolog
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Setting up Prolog with Java
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Executing Prolog queries using Java
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Summary
4
Interfacing with Weka
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Interfacing with Weka
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An introduction to Weka
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Installing and interfacing with Weka
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Reading and writing datasets
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Converting datasets
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Summary
5
Handling Attributes
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Handling Attributes
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Filtering attributes
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Discretizing attributes
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Attribute selection
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Summary
6
Supervised Learning
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Supervised Learning
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Developing a classifier
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Model evaluation
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Making predictions
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Loading and saving models
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Summary
7
Semi-Supervised and Unsupervised Learning
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Semi-Supervised and Unsupervised Learning
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Working with k-means clustering
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Evaluating a clustering model
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An introduction to semi-supervised learning
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The difference between unsupervised and semi-supervised learning
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Self-training and co-training machine learning models
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Making predictions with semi-supervised machine learning models
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Summary
8
Other Books You May Enjoy
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Making predictions with semi-supervised machine learning models

Now, we'll look into how to make predictions using our trained model. Consider the following code:

import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.classifiers.collective.functions.LLGC;
import weka.classifiers.collective.evaluation.Evaluation;

We will be importing two JAR libraries, as follows:

  • The weka.jar library
  • The collective-classification-<date>.jar library

Therefore, we will take the two base classes, Instances and DataSource, and we will use the LLGC class (since we have trained our model using LLGC) from the collective-classifications package, as well as the Evaluation class from the collective-classifications package.

We will first assign an ARFF file to our DataSource object; we'll read it into the memory, in an Instances object. We'll assign a class attribute to our Instances object, and then, we will build our model:

public static void main(String[] args) {
    try{...
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