IEEE

Unsupervised learning of Gaussian Mixture Models: Evolutionary Create and Eliminate for Expectation Maximization algorithm

Abstract: This paper describes the Evolutionary Create & Eliminate for Expectation Maximization algorithm (ECE-EM) for learning finite Gaussian Mixture Models (GMMs). The proposed algorithm is a ...
IEEE

A fully-pipelined expectation-maximization engine for Gaussian Mixture Models

Abstract: Gaussian Mixture Models (GMMs) are powerful tools for probability density modeling and soft clustering. They are widely used in data mining, signal processing and computer vision. In many ...
GitHub

sohampradhan27/NoPackages-GMM-EM

A complete from-scratch implementation of Gaussian Mixture Models (GMM) using the Expectation-Maximization (EM) algorithm in Python. This implementation uses only NumPy for mathematical operations and ...
Risk

A new way to calculate conditional expectations

As acronyms go, GMM-DCKE – Gaussian mixture model dynamically controlled kernel estimation – is a bit of a mouthful. Its proponents, though, consider it to be the simplest expression of conditional ...
Concordia University

Master Thesis Defense - March 25, 2015: Variational Learning for Finite Inverted Dirichlet Mixture Models and Its Applications

You are invited to attend the following M.A.Sc. (Information Systems Security) thesis examination. Clustering is an important step in data mining, machine learning, computer vision and image ...