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Machine Learning for OpenCV

Machine Learning for OpenCV

By : Michael Beyeler, Michael Beyeler (USD)
4.4 (13)
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Machine Learning for OpenCV

Machine Learning for OpenCV

4.4 (13)
By: Michael Beyeler, Michael Beyeler (USD)

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 (21 chapters)
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Title Page
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Assessing the significance of our results


Assume for a moment that we implemented the cross-validation procedure for two versions of our k-NN classifier. The resulting test scores are-- 92.34% for Model A, and 92.73% for Model B. How do we know which model is better?

Following our logic introduced here, we might argue for Model B because it has a better test score. But what if the two models are not significantly different? These could have two underlying causes, which are both a consequence of the randomness of our testing procedure:

  • For all we know, Model B just got lucky. Perhaps we chose a really low k for our cross-validation procedure. Perhaps Model B ended up with a beneficial train-test split so that the model had no problem classifying the data. After all, we didn't run tens of thousands of iterations like in bootstrapping to make sure the result holds in general.
  • Variability in the test scores is so high that we cannot know for sure whether the two results are essentially the same...

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