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Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science 2. Data Acquisition FREE CHAPTER 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...

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