In this chapter, we've seen how to apply unsupervised learning algorithms on neural networks. We've been presented a new and suitable architecture for that end, the self-organizing maps of Kohonen. Unsupervised learning has proved to be as powerful as the supervised learning methods, because they concentrate only on the input data, without need to make input-output mappings. We've seen graphically how the training algorithms are able to drive the weights nearer to the input data, thereby playing a role in clustering and dimensionality reduction. In addition to these examples, Kohonen SOMs are also able to classify clusters of data, as each neuron will provide better responses for a particular set of inputs.
Neural Network Programming with Java - Second Edition
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Neural Network Programming with Java - Second Edition
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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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Current Title:
Neural Network Programming with Java - Second Edition
Table of Contents (19 chapters)
Neural Network Programming with Java Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Getting Started with Neural Networks
Getting Neural Networks to Learn
Perceptrons and Supervised Learning
Self-Organizing Maps
Forecasting Weather
Classifying Disease Diagnosis
Clustering Customer Profiles
Text Recognition
Optimizing and Adapting Neural Networks
Current Trends in Neural Networks
References
Index
Customer Reviews