Lecture

Support Vector Regression: Recap and Convex Optimization

Description

This lecture covers the recap of Support Vector Regression (SVR) with a focus on the primal and dual formulations, stationary conditions, and the concept of convex-quadratic programming. It also delves into the duality of SVR, the primal Lagrangian, and the duality gap. The instructor explains the duality between SVR and Gaussian Process Regression (GPR), showcasing how SVR can be equivalent to GPR under certain conditions.

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Official source

https://mediaspace.epfl.ch/media/0_z77eum7v

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Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Statistical inference: Mathematical statistics

Theoretical computer science

Algorithms and data structures: Combinatorial optimization

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