Abstract
The intuition behind Semi-Supervised Support Vector Machines (S3VMs) is very simple. Figure 6.1(a) shows a completely labeled dataset. If we were to draw a straight line to separate the two classes, where should the line be? One reasonable place is right in the middle, such that its distance to the nearest positive or negative instance is maximized. This is the linear decision boundary found by Support Vector Machines (SVMs), and is shown in Figure 6.1(a). The figure also shows two dotted lines that go through the nearest positive and negative instances. The distance from the decision boundary to a dotted line is called the geometric margin. As mentioned above, this margin is maximized by SVMs.
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© 2009 Springer Nature Switzerland AG
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Zhu, X., Goldberg, A.B. (2009). Semi-Supervised Support Vector Machines. In: Introduction to Semi-Supervised Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-01548-9_6
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DOI: https://doi.org/10.1007/978-3-031-01548-9_6
Publisher Name: Springer, Cham
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