Book Image

Machine Learning for OpenCV

By : Michael Beyeler
Book Image

Machine Learning for OpenCV

By: Michael Beyeler

Overview of this book

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Table of Contents (13 chapters)

Dealing with nonlinear decision boundaries

What if the data cannot be optimally partitioned using a linear decision boundary? In such a case, we say the data is not linearly separable.

The basic idea to deal with data that is not linearly separable is to create nonlinear combinations of the original features. This is the same as saying we want to project our data to a higher-dimensional space (for example, from 2D to 3D) in which the data suddenly becomes linearly separable. This concept is illustrated in the following figure:

Finding linear hyperplanes in higher-dimensional spaces

If data in its original input space (left) cannot be linearly separated, we can apply a mapping function ϕ(.) that projects the data from 2D into a 3D plane. In this higher-dimensional space, we may find that there is now a linear decision boundary (which, in 3D, is a plane) that can separate...