Lecture

Support Vector Machines: SVMs

In course

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Description

This lecture covers the concepts of hard-margin and soft-margin Support Vector Machines (SVMs), including the geometric construction, calculation of the margin, optimization problems, support vectors, slack variables, and imbalanced classification. It also discusses the hinge loss interpretation, risks comparison, and the quadratic hinge loss in SVMs.

Official source

https://mediaspace.epfl.ch/media/0_q7o0uihl

About this result

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Ontological neighbourhood

Information engineering

Machine learning: Topics in machine learning

Theoretical computer science

Algorithms and data structures: Combinatorial optimization

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