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Java for Data Science

You're reading from  Java for Data Science

Product type Book
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Pages 386 pages
Edition 1st Edition
Languages
Authors (2):
Richard M. Reese Richard M. Reese
Profile icon Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
Profile icon Jennifer L. Reese
View More author details

Table of Contents (19) Chapters

Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Data Science 2. Data Acquisition 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

Additional network architectures and algorithms


We have discussed a few of the most common and practical neural networks. At this point, we would also like to consider some specialized neural networks and their application in various fields of study. These types of networks do not fit neatly into one particular category, but may still be of interest.

The k-Nearest Neighbors algorithm

An artificial neural network implementing the k-NN algorithm is similar to MLP networks, but it provides significant reduction in time compared to the winner takes all strategy. This type of network does not require a training algorithm after the initial weights are set and has fewer connections among its neurons. We have chosen not to provide an example of this algorithm's implementation because its use in Weka is very similar to the MLP example.

This type of network is best suited to classification tasks. Because it utilizes lazy learning techniques, reserving all computation until after information has been...

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