September 2019
Intermediate to advanced
420 pages
10h 29m
English
book
Machine Learning for OpenCV 4 - Second Edition
by Aditya Sharma, Michael Beyeler (USD), Vishwesh Ravi Shrimali, Michael Beyeler
Content preview from Machine Learning for OpenCV 4 - Second Edition
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Dealing with nonlinear decision boundaries
What if the data cannot be optimally partitioned using a linear decision boundary? In such a case, we say the data is not linearly separable.
The basic idea to deal with data that is not linearly separable is to create nonlinear combinations of the original features. This is the same as saying we want to project our data to a higher-dimensional space (for example, from 2D to 3D), in which the data suddenly becomes linearly separable.
This concept is illustrated in the following diagram:
The preceding diagram shows how to find linear hyperplanes in higher-dimensional spaces. If data in its original ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.