A Cython Machine Learning library dedicated to Hidden Markov Models

Implemented Gaussian Mixture Models (GMM) for image color segmentation.

A machine learning clustering model for customer segmentation to define marketing strategy.

Infinite Mixtures of Infinite Factor Analysers

Suite for human/nonhuman binary classification problem using MOG, CNN with VIRAT2.0-based dataset

Coordinate ascent mean-field variational inference (CAVI) using the evidence lower bound (ELBO) to iteratively perform the optimal variational factor distribution parameter updates for clustering.

Code and data for "Superclass-class conditional Gaussian mixture model for learning fine-grained embeddings" @ ICLR2022

Differential Evolution Clustering

This repository contains functions/codes related to different methods of machine learning for classification and clustering in python.

Library and hand-made clustering algorithms are implemented in this project

Synthetic Data Generation (SDG) by Gaussian Mixture Model (GMM) Distribution

Codes for simulation studies to examine the performance of the EM algorithm and its modifications Classification EM and Stochastic EM for Gaussian mixture and a mixture of Markov chains.

Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Models via Fully Homomorphic Encryption

Algorithms proposed in the following paper: G. Oliveira, L. L. Minku and A. L. I. Oliveira, "Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model Approach," in IEEE Transactions on Knowledge and Data Engineering, 2021. doi: 10.1109/TKDE.2021.3099690.

Detecting stock market phases using a Gaussian mixture model.

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

Fast explication of Gaussian Mixture model for clustering

Image classification of Iris Flower dataset using 5 different machine learning methods (Decision Tree, Support Vector Machine, Random Forest, Naive Bayes and K-nearest neighbour)

Implementation of EM algorithm for the Gaussian mixture model.

The R code sets for "Inverse Gaussian quadrature and finite normal-mixture approximation of the generalized hyperbolic distribution"