Learn about support vector machines in this comprehensive lecture covering margin classifiers, linear separators, and dual representation. Explore the mathematical foundations of SVMs, including measuring distances, equivalent optimization, and the Lagrangian approach. Gain insights into inner minimization techniques and their application to classification problems. Discover how SVMs work and their importance in machine learning through this in-depth exploration of their principles and implementation.
Overview
Syllabus
Introduction
What are support vector machines
Margin classifiers
Linear separators
Measuring distances
Equivalent optimization
Support vector machines
Dual representation
Lagrangian
Inner minimization
Summary
Classification
Taught by
Pascal Poupart
