Book Image

Neural Network Programming with Java - Second Edition

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

Neural Network Programming with Java - Second Edition

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

Overview of this book

<p>Want to discover the current state-of-art in the field of neural networks that will let you understand and design new strategies to apply to more complex problems? This book takes you on a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java, giving you everything you need to stand out.</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 practical examples. Further on, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning, 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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Table of Contents (19 chapters)
Neural Network Programming with Java Second Edition
Neural Network Programming with Java Second Edition
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Getting Started with Neural Networks
Getting Started with Neural Networks
Discovering neural networks
Why artificial neural networks?
From ignorance to knowledge – learning process
Let the coding begin! Neural networks in practice
The neuron class
The NeuralLayer class
The ActivationFunction interface
The neural network class
Time to play!
Summary
2
Getting Neural Networks to Learn
Getting Neural Networks to Learn
Learning ability in neural networks
Learning paradigms
The learning process
Examples of learning algorithms
Time to see the learning in practice!
Amazing, it learned! Or, did it really? A further step – testing
Summary
3
Perceptrons and Supervised Learning
Perceptrons and Supervised Learning
Supervised learning – teaching the neural net
A basic neural architecture – perceptrons
Multi-layer perceptrons
Learning in MLPs
Practical example 1 – the XOR case with delta rule and backpropagation
Practical example 2 – predicting enrolment status
Summary
4
Self-Organizing Maps
Self-Organizing Maps
Neural networks unsupervised learning
Unsupervised learning algorithms
Kohonen self-organizing maps
Summary
5
Forecasting Weather
Forecasting Weather
Neural networks for regression problems
Loading/selecting data
Choosing input and output variables
Preprocessing
Empirical design of neural networks
Summary
6
Classifying Disease Diagnosis
Classifying Disease Diagnosis
Foundations of classification problems
Logistic regression
Neural networks for classification
Disease diagnosis with neural networks
Summary
7
Clustering Customer Profiles
Clustering Customer Profiles
Clustering tasks
Applied unsupervised learning
Profiling
Summary
8
Text Recognition
Text Recognition
Pattern recognition
Neural networks in pattern recognition
Summary
9
Optimizing and Adapting Neural Networks
Optimizing and Adapting Neural Networks
Common issues in neural network implementations
Input selection
Online retraining
Adaptive neural networks
Summary
10
Current Trends in Neural Networks
Current Trends in Neural Networks
Deep learning
Deep architectures
Implementing a hybrid neural network
Summary
References
References
Chapter 1: Getting Started with Neural Networks
Chapter 2: Getting Neural Networks to Learn
Chapter 3: Perceptrons and Supervised Learning
Chapter 4: Self-Organizing Maps
Chapter 5: Forecasting Weather
Chapter 6: Classifying Disease Diagnosis
Chapter 7: Clustering Customer Profiles
Chapter 8: Text Recognition
Chapter 9: Optimizing and Adapting Neural Networks
Chapter 10: Current Trends in Neural Networks
Index
Index

Chapter 9: Optimizing and Adapting Neural Networks

Utrans, J, Moody J., Rehfuss, S., Siegelmann, H., Input variable selection for neural networks: application to predicting the U.S. business cycle, Computational Intelligence for Financial Engineering, Proceedings of the IEEE/IAFE 1995.

Saxén, H., Pettersson, F., Method for the selection of inputs and structure of feedforwaed neural networks, Computers and Chemical Enginnering, Vol. 30, Is. 6-7, pp. 1048-1045, 15May 2006.

Souza, Alan M.F., Affonso, Carolina M., Soares, Fábio M., De Oliveira, Roberto C.L., Soft Sensor for Fluoridated Alumina Inference in Gas Treatment Centers, Intelligent Data Engineering and Automated Learning 2012, Lecture Notes in Computer Science v. 7435, pp. 294-302, Spinger Verlab Berlin Heidelberg, 2012.

Jollife, I.T. Principal Component Analysis, 2nd ed. Springer Wiley, 2002.

Karmin E.D., A simple procedure for pruning back-propagation trained neural networks, IEEE transactions on Neural Networks, pp. 239-242, June 1990

Gill, P.E., Murray, W. Wright, M.H., Practical Optimization, Academic Press: London, 1981.

Carpenter, Gail A., Grossberg, Stephen, Adaptive Resonance Theory, The Handbook of Brain Theory and Neural Networks, 2nd ed., pp. 1-11, 2002.

Zhang, Guoqiang, Hu, Michael Y., Patuwo, Eddy B., Indro, Daniel C., Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis, European Journal of Operational Research, vol. 116, Is. 1, pp. 16-32, July 1999.

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