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Gaussian Mixture Models(GMM)

Analytics Vidhya

Brief: Gaussian mixture models is a popular unsupervised learning algorithm. The GMM approach is similar to K-Means clustering algorithm, but is more robust and therefore useful due to…

Gaussian Mixture Models(GMM)

Level Up Coding

Brief: Gaussian mixture models is a popular unsupervised learning algorithm. The GMM approach is similar to K-Means clustering algorithm, but is more robust and therefore useful due to…

2.1. Gaussian mixture models

Scikit-learn User Guide

sklearn.mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample them, and estimate them from data. Facilit......

In Depth: Gaussian Mixture Models

Python Data Science Handbook

The k -means clustering model explored in the previous section is simple and relatively easy to understand, but its simplicity leads to practical challenges in its application. In particular, the non-...

Gaussian Mixture Modelling (GMM)

Towards Data Science

In a previous post, I discussed k-means clustering as a way of summarising text data. I also talked about some of the limitations of k-means and in what situations it may not be the most appropriate…

Gaussian Mixture Model Selection

Scikit-learn Examples

Gaussian Mixture Model Selection This example shows that model selection can be performed with Gaussian Mixture Models using information-theoretic criteria (BIC) . Model selection concerns both the co...

Gaussian Mixture Models for Clustering

Towards Data Science

Recently I was using K-Means in a project and decided to see what other options were out there for clustering algorithms. I always find it enjoyable to sink my teeth into expanding my data science…

Gaussian Mixture Models Explained

Towards Data Science

In the world of Machine Learning, we can distinguish two main areas: Supervised and unsupervised learning. The main difference between both lies in the nature of the data as well as the approaches…

A Simple Introduction to Gaussian Mixture Model (GMM)

Towards Data Science

A Gaussian distribution is what we also know as the Normal distribution. You know, that well spread concept of a bell shaped curve with the mean and median as central point. Given that, if we look at…...

Gaussian Mixture Models (GMMs): from Theory to Implementation

Towards Data Science

Mixture Models A mixture model is a probability model for representing data that may arise from several different sources or categories, each of which is modeled by a separate probability distribution...

Gaussian Mixture Models: implemented from scratch

Towards Data Science

From the rising of the Machine Learning and Artificial Intelligence fields Probability Theory was a powerful tool, that allowed us to handle uncertainty in a lot of applications, from classification…

Model-Based Clustering using GMM-Gaussian Mixture Models

Level Up Coding

A Gaussian Mixture Model (GMM) is a clustering technique that assumes data is generated from a mixture of several Gaussian distributions, each with its own mean and covariance. GMMs are widely used in...