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)

Understanding ensemble methods

The goal of ensemble methods is to combine the predictions of several individual estimators built with a given learning algorithm in order to solve a shared problem. Typically, an ensemble consists of two major components:

  • a set of models
  • a set of decision rules that govern how the results of these models are combined into a single output
The idea behind ensemble methods has much to do with the wisdom of the crowd concept. Rather than the opinion of a single expert, we consider the collective opinion of a group of individuals. In the context of machine learning, these individuals would be classifiers or regressors. The idea is that if we just ask a large enough number of classifiers, one of them ought to get it right.

A consequence of this procedure is that we get a multitude of opinions about any given problem. So how do we know which classifier...