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About Lecture Notes SVM
This document provides an overview of support vector machines (SVM) for classification problems. It discusses how SVM seeks to find an optimal separating hyperplane with the maximum margin between classes in the training data. When the data is not perfectly separable, SVM allows for some misclassifications by introducing slack variables and penalizing them. The document formulates SVM as both a primal and dual optimization problem and explains how the dual problem leads to a sparse solution with only support vectors determining the optimal hyperplane.
Detailed Information
| Author: | ['Lilia Xa'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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