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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Practical examples of regression, clustering and neural networks

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Length 382 pages
Edition 1st Edition
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Authors (2):
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Pavan Kumar Kolluru Pavan Kumar Kolluru
Author Profile Icon Pavan Kumar Kolluru
Pavan Kumar Kolluru
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (10) Chapters Close

Preface 1. Getting Started with MATLAB Machine Learning FREE CHAPTER 2. Importing and Organizing Data in MATLAB 3. From Data to Knowledge Discovery 4. Finding Relationships between Variables - Regression Techniques 5. Pattern Recognition through Classification Algorithms 6. Identifying Groups of Data Using Clustering Methods 7. Simulation of Human Thinking - Artificial Neural Networks 8. Improving the Performance of the Machine Learning Model - Dimensionality Reduction 9. Machine Learning in Practice

Exporting data from MATLAB

Many of the functions we have used to import data into MATLAB have a corresponding function that allows us to export data. At the beginning of the chapter, we learned to save our data for later use with the save command:

>> save filename.mat

Remember, this command saves all contents of the workspace in a compressed file with a .mat extension, called a MAT-file.

The dlmread() function allows us to handle text files with a specified delimiter. We can use this function to write a matrix to an ASCII-delimited file. To test the function, we start from a matrix of random numbers:

>> MyMatrix = rand(5)
MyMatrix =
0.7577 0.7060 0.8235 0.4387 0.4898
0.7431 0.0318 0.6948 0.3816 0.4456
0.3922 0.2769 0.3171 0.7655 0.6463
0.6555 0.0462 0.9502 0.7952 0.7094
0.1712 0.0971...
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