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      • Machine learning of mechanical properties of steels
      • Online prediction of mechanical properties of hot rolled steel plate using machine learning
      • Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural
      • Tempcore, new process for the production of high quality reinforcing
      • TEMPCORE, the most convenient process to produce low cost high strength rebars from 8 to 75 mm
      • Experimental investigation and simulation of structure and tensile properties of Tempcore treated re
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  • Lecture Note
  • Gaussian Mixture Model
  • Example: Object segmentation
  • E-M algorithm for Gaussian Mixture Kernel
  • Reference

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  1. Machine Learning
  2. ML Notes
  3. Parameter, Density Estimation

Gaussian Mixture Model

PreviousMAP, MLENextE-M

Last updated 3 years ago

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Lecture Note

GMM-EM class note

Gaussian Mixture Model

Click here for PPT slides: TAMU

Click here for PPT slides: VTech

Gaussian Distribution

The density can be estimated by multiples of Gaussian Kernels

π\piπis prior, not likelihood.

The mixing coefficients are themselves probabilities and must meet this condition: sum(pi)=1

How to find the optimal parameter Θ\ThetaΘ?

Lets use the Maximum Likelihood Estimation to find the optimal parameter. This can be solved by E-M algorithm/

Example: Object segmentation

see PPT slides: VTech

E-M algorithm for Gaussian Mixture Kernel

Read here for detail

Reference

The Hundred-Page Machine Learning Book http://themlbook.com/wiki/doku.php

머신러닝/패턴인식, 오일석

https://towardsdatascience.com/gaussian-mixture-models-explained-6986aaf5a95towardsdatascience.com