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)

Implementing AdaBoost

When the trees in the forest are trees of depth 1 (also known as decision stumps) and we perform boosting instead of bagging, the resulting algorithm is called AdaBoost.

AdaBoost adjusts the dataset at each iteration by performing the following actions:

  • Selecting a decision stump
  • Increasing the weighting of cases that the decision stump labeled incorrectly while reducing the weighting of correctly labeled cases

This iterative weight adjustment causes each new classifier in the ensemble to prioritize training the incorrectly labeled cases. As a result, the model adjusts by targeting highly-weighted data points.

Eventually, the stumps are combined to form a final classifier.

Implementing AdaBoost in OpenCV

...