Lecture

Gaussian Mixture Regression: Theory and Applications

Description

This lecture covers Gaussian mixture regression, starting with learning the joint distribution and computing the regressive signal. It explores the impact of different covariance matrices on the regressive curve and discusses overfitting with multiple Gauss functions.

Official source

https://mediaspace.epfl.ch/media/0_4gwqw90m

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

Statistics

Data analysis: Regression analysis

Statistical inference: Mathematical statistics

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