In order to understand how SVMs work, we have to think about decision boundaries. When we used linear classifiers or decision trees in earlier chapters, our goal was always to minimize the classification error. We did this by assessing the accuracy using mean squared error. An SVM tries to achieve low classification errors too, but it does so only implicitly. An SVM's explicit objective is to maximize the margins between data points of
Machine Learning for OpenCV 4
By :
Machine Learning for OpenCV 4
By:
Overview of this book
OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition.
You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system.
By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.
Table of Contents (18 chapters)
Preface
Free Chapter
Section 1: Fundamentals of Machine Learning and OpenCV
A Taste of Machine Learning
Working with Data in OpenCV
First Steps in Supervised Learning
Representing Data and Engineering Features
Section 2: Operations with OpenCV
Using Decision Trees to Make a Medical Diagnosis
Detecting Pedestrians with Support Vector Machines
Implementing a Spam Filter with Bayesian Learning
Discovering Hidden Structures with Unsupervised Learning
Section 3: Advanced Machine Learning with OpenCV
Using Deep Learning to Classify Handwritten Digits
Ensemble Methods for Classification
Selecting the Right Model with Hyperparameter Tuning
Using OpenVINO with OpenCV
Conclusion
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