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)

Combining Different Algorithms into an Ensemble

So far, we have looked at a number of interesting machine learning algorithms, from classic methods such as linear regression to more advanced techniques such as deep neural networks. At various points, we pointed out that every algorithm has its own strengths and weaknesses--and we took note of how to spot and overcome these weaknesses.

However, wouldn't it be great if we could simply stack together a bunch of average classifiers to form a much stronger ensemble of classifiers?

In this chapter, we will do just that. Ensemble methods are techniques that bind multiple different models together in order to solve a shared problem. Their use has become a common practice in competitive machine learning--making use of an ensemble typically improves an individual classifier's performance by a small percentage.

Among these techniques...