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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
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…