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Multiclass Classification with Support Vector Machines (SVM), Dual Problem and Kernel Functions

Finally understand the concept behind SVM + Implementation in Python via scikit-learn

Hucker Marius
Towards Data Science
11 min readJun 9, 2020

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source: unsplash (Bekky Bekks)

Support Vector Machines (SVM) are not new but are still a powerful tool for classification due to their tendency not to overfit, but to perform well in many cases. If you are only interested in a certain topic, just scroll over the topics. These are the topics in chronological order:

  • What’s the mathematical concept behind the Support Vector Machine?
  • What is a kernel and what are kernel functions?
  • What is the kernel trick?
  • What is the dual problem of a SVM?
  • How does Multiclass Classification take place?
  • Implementation via Python and scikit-learn

If you are only interested in how it can be implemented using Python and scikit-learn, scroll down to the end!

Let’s get started.

The objective is to find a hyperplane in an n-dimensional space that separates the data points to their potential classes. The hyperplane should be positioned with the maximum distance to the data points. The data points with the minimum…

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Hucker Marius
Hucker Marius

Written by Hucker Marius

Data Scientist @Atruvia | Tech & Marketing Enthusiast from Karlsruhe, Germany https://huks.digital/webdesign-agentur-karlsruhe

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