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MATLAB for Machine Learning

You're reading from  MATLAB for Machine Learning

Product type Book
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Pages 382 pages
Edition 1st Edition
Languages
Authors (2):
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
Pavan Kumar Kolluru Pavan Kumar Kolluru
Profile icon Pavan Kumar Kolluru
View More author details
Toc

Table of Contents (17) Chapters close

Title Page
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with MATLAB Machine Learning 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

Working with media files


Images, videos, and audio files nowadays are an integral part of our lives. We use them widely thanks to the capabilities provided by modern devices that enable us to capture, store, and manipulate them. They often represent the input data we use in our machine learning applications, because through such algorithms, it is possible to extract a lot of information that at first glance is not so obvious. In this section, we will learn to deal with such files in the MATLAB environment.

Handling images

Raster images are made up of a grid of colored pixels. In MATLAB, images can be represented as two-dimensional matrices, where each matrix element corresponds to a single pixel of the displayed image. For example, a 800x600 photo (consisting of 600 lines and 800 columns of pixel of different colors) will be stored as a 600x800 matrix. However, sometimes, a third dimension will be required to store the depth of color. For example, for RGB images, you will need to specify red...

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