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Computer Vision Projects with OpenCV and Python 3

You're reading from   Computer Vision Projects with OpenCV and Python 3 Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789954555
Length 182 pages
Edition 1st Edition
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Author (1):
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Matthew Rever Matthew Rever
Author Profile Icon Matthew Rever
Matthew Rever
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Table of Contents (9) Chapters Close

Preface 1. Setting Up an Anaconda Environment FREE CHAPTER 2. Image Captioning with TensorFlow 3. Reading License Plates with OpenCV 4. Human Pose Estimation with TensorFlow 5. Handwritten Digit Recognition with scikit-learn and TensorFlow 6. Facial Feature Tracking and Classification with dlib 7. Deep Learning Image Classification with TensorFlow 8. Other Books You May Enjoy

Creating and training a support vector machine

In this section, we're going to create and train a support vector machine that will actually perform our digit classification.

In the very first example, we're going to use scikit-learn, and we're going to use what's called a support vector machine, which is a very powerful, very versatile classic machine learning technique that can learn all kinds of functions and all kinds of mappings from inputs to outputs. We're going to do classification, which is mapping inputs as an array of pixels, and in our case we're going to classify each input into one of ten classes, corresponding to ten digits. But we can classify different kinds of things as continuous ordered functions, which is called regression, and that can be useful, for example, if you want to extract position or an area of volume where it doesn...

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