Book Image

Machine Learning for OpenCV - Supervised Learning [Video]

By : Michael Beyeler
Book Image

Machine Learning for OpenCV - Supervised Learning [Video]

By: Michael Beyeler

Overview of this book

<p>Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to Medical diagnosis, this has been widely used in various domains.</p> <p>This course will take you right from the essential concepts of statistical learning to help you with various algorithms to implement it with other OpenCV tasks.</p> <p>The course will also guide you through creating custom graphs and visualizations, and show you how to go from the raw data to beautiful visualizations. We will also build a machine learning system that can make a medical diagnosis.</p> <p>By the end of this course, you will be ready create your own ML system and will also be able to take on your own machine learning problems.</p> <p>All the code and supporting files for this course are available on Github at <a style="color: #fa8d11;" href="https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Supervised-Learning" target="blank">https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Supervised-Learning</a></p> <h2>Style and Approach</h2> <p>This course walks you through the key elements of OpenCV and its powerful Machine Learning classes while demonstrating how to get to grips with a range of models.</p>
Table of Contents (6 chapters)
Chapter 6
Detecting Pedestrians with Support Vector Machines
Content Locked
Section 2
Dealing with Nonlinear Decision Boundaries
One problem with mapping approach is that it is impractical in large dimensions, because it adds a lot of extra terms to do the mathematical projections between the dimensions. This is where the so-called kernel trick comes in to play. In this video, we will get a quick overview about the Kernels trick. Then we will implement non-linear SVM’s. - Implement nonlinear SVM - Plot decision boundary in a 2 by 2 subplot