An important subfield of machine learning is supervised learning. In supervised learning, we try to learn from a set of labeled training data; that is, every data sample has a desired target value or true output value. These target values could correspond to the continuous output of a function (such as y
in y = sin(x)
), or to more abstract and discrete categories (such as cat or dog). If we are dealing with continuous output, the process is called regression, and if we are dealing with discrete output, the process is called
classification. Predicting housing prices from sizes of houses is an example of regression. Predicting the species from the color of a fish would be classification. In this chapter, we will focus on classification using SVMs.
OpenCV with Python Blueprints
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OpenCV with Python Blueprints
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Overview of this book
Table of Contents (14 chapters)
OpenCV with Python Blueprints
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Fun with Filters
Hand Gesture Recognition Using a Kinect Depth Sensor
Finding Objects via Feature Matching and Perspective Transforms
3D Scene Reconstruction Using Structure from Motion
Tracking Visually Salient Objects
Learning to Recognize Traffic Signs
Learning to Recognize Emotions on Faces
Index
Customer Reviews