Book Image

Neural Network Programming with Java

By : Alan M. F. Souza, Fabio M. Soares
Book Image

Neural Network Programming with Java

By: Alan M. F. Souza, Fabio M. Soares

Overview of this book

<p>Vast quantities of data are produced every second. In this context, neural networks become a powerful technique to extract useful knowledge from large amounts of raw, seemingly unrelated data. One of the most preferred languages for neural network programming is Java as it is easier to write code using it, and most of the most popular neural network packages around already exist for Java. This makes it a versatile programming language for neural networks.</p> <p>This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java.</p> <p>You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using the concepts you’ve learned. Furthermore, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.</p> <p>All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.</p>

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Neural Network Programming with Java
Alan M. F. Souza | Fabio M. Soares
Jan, 2016 | 244 pages
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Table of Contents (19 chapters)
Neural Network Programming with Java
Neural Network Programming with Java
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with Neural Networks
Getting Started with Neural Networks
Discovering neural networks
Why artificial neural network?
How neural networks are arranged
Learning about neural network architectures
From ignorance to knowledge – learning process
Let the implementations begin! Neural networks in practice
Summary
2
How Neural Networks Learn
How Neural Networks Learn
Learning ability in neural networks
Learning paradigms
Systematic structuring – learning algorithm
Examples of learning algorithms
Coding of the neural network learning
Two practical examples
Summary
3
Handling Perceptrons
Handling Perceptrons
Studying the perceptron neural network
Popular multilayer perceptrons (MLPs)
Interesting MLP applications
Learning process in MLPs
Hands-on MLP implementation!
Levenberg–Marquardt implementation
Practical application – types of university enrolments
Summary
4
Self-Organizing Maps
Self-Organizing Maps
Neural networks' unsupervised way of learning
Some unsupervised learning algorithms
Kohonen self-organizing maps (SOMs)
Coding of the Kohonen algorithm
Summary
5
Forecasting Weather
Forecasting Weather
Neural networks for prediction problems
No data, no neural net – selecting data
Adjusting values – data preprocessing
Java implementation for weather prediction
Empirical design of neural networks
Summary
6
Classifying Disease Diagnosis
Classifying Disease Diagnosis
What are classification problems, and how can neural networks be applied to them?
A special type of activation function – Logistic regression
Applying neural networks for classification
Disease diagnosis with neural networks
Summary
7
Clustering Customer Profiles
Clustering Customer Profiles
Clustering task
Applied unsupervised learning
Customer profiling
Implementation in Java
Summary
8
Pattern Recognition (OCR Case)
Pattern Recognition (OCR Case)
What is pattern recognition all about?
How to apply neural networks in pattern recognition
The OCR problem
Let the coding begin!
Summary
9
Neural Network Optimization and Adaptation
Neural Network Optimization and Adaptation
Common issues in neural network implementations
Input selection
Structure selection
Online retraining
Adaptive neural networks
Summary
Setting up the NetBeans Environment
Setting up the NetBeans Environment
Download and install NetBeans
Setting up the NetBeans environment
Importing a project
Programming and running code with NetBeans
Debugging with NetBeans
Setting Up the Eclipse Environment
Setting Up the Eclipse Environment
Download and install Eclipse
Setting up the Eclipse environment
Importing a project
Programming and running code with the Eclipse IDE
Debugging with the Eclipse IDE
References
References
Chapter 1 - Getting Started with Neural Networks
Chapter 2 - How Neural Networks Learn
Chapter 3 - Working with Perceptrons
Chapter 4 - Self-Organizing Maps
Chapter 5 - Forecasting Weather
Chapter 6 - Disease Diagnosis
Chapter 7 - Clustering Customer Profiles
Chapter 8 - Pattern Recognition (the OCR Case)
Chapter 9 - Neural Network Optimization and Adaptation
Index
Index

Input selection

One of the key tasks in designing a neural network application is to select appropriate inputs. For the unsupervised case, one wishes to use only relevant variables on which the neural network will find the patterns. For the supervised case, there is a need to map the outputs to the inputs, so one needs to choose only the input variables that somewhat influence the output.

Data correlation

One strategy that helps in selecting good inputs in the supervised case is the correlation between data series. A correlation between data series is a measure of how one data sequence reacts or influences the other. Suppose that we have one dataset containing a number of data series from which we choose one to be an output. Now, we need to select the inputs from the remaining variables.

We then evaluate the influence of one variable at a time on the output in order to decide whether to include it as an input or not. The Pearson coefficient is one of the most used variables:

Where Sx(k)y(k)...

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