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In the previous articles we discussed in detail the Dirichlet Process Mixture Models and how they can be used in cluster analysis. In this article we will present a Java implementation of two different DPMM models: the Dirichlet Multivariate Normal Mixture Model which can be used to cluster Gaussian data and the Dirichlet-Multinomial Mixture Model […]
Clustering with Dirichlet Process Mixture Model in Java Read More »
I am happy to announce that the Datumbox Machine Learning Framework is now open sourced under GPL 3.0 and you can download its code from Github! What is this Framework? The Datumbox Machine Learning Framework is an open-source framework written in Java which enables the rapid development of Machine Learning models and Statistical applications. It
New open-source Machine Learning Framework written in Java Read More »
In this guide we are going to discuss how to install and use the Datumbox Machine Learning framework in your Java projects. Since almost all of the code is written in Java, using it is as simple as including it as dependency in your Java project. Nevertheless a couple of classes (DataEnvelopmentAnalysis and LPSolver) use
How to install and use the Datumbox Machine Learning Framework Read More »
The new version of Datumbox Machine Learning Framework has been released! Download it now from Github or Maven Central Repository. What is new? The main focus of version 0.6.0 is to extend the Framework to handle Large Data, improve the code architecture and the public APIs, simplify data parsing, enhance the documentation and move to
Datumbox Machine Learning Framework 0.6.0 Released Read More »
The new version of Datumbox Machine Learning Framework has been released! Download it now from Github or Maven Central Repository. What is new? The main focus of version 0.6.1 is to resolve various bugs, reduce memory consumption and improve speed. Let’s see in detail the changes of this version: Bug Fixes: A minor issue related
Datumbox Machine Learning Framework 0.6.1 Released Read More »
By now most of you have heard/played the 2048 game by Gabriele Cirulli. It’s a simple but highly addictive board game which requires you to combine the numbers of the cells in order to reach the number 2048. As expected the difficulty of the game increases as more cells are filled with high values. Personally
Using Artificial Intelligence to solve the 2048 Game (JAVA code) Read More »
In the ISO research project for my MSc in Machine Learning at Imperial College London, I focused on the problem of Cluster Analysis by using Dirichlet Process Mixture Models. The DPMMs is a “fully-Bayesian” unsupervised learning technique which unlike other Cluster Analysis methods does not require us to predefine the total number of clusters within
Overview of Cluster Analysis and Dirichlet Process Mixture Models Read More »
This blog post is the second part of an article series on Dirichlet Process mixture models. In the previous article we had an overview of several Cluster Analysis techniques and we discussed some of the problems/limitations that rise by using them. Moreover we briefly presented the Dirichlet Process Mixture Models, we talked about why they
Finite Mixture Model based on Dirichlet Distribution Read More »
This article is the third part of the series on Clustering with Dirichlet Process Mixture Models. The previous time we defined the Finite Mixture Model based on Dirichlet Distribution and we posed questions on how we can make this particular model infinite. We briefly discussed the idea of taking the limit of the model when
The Dirichlet Process the Chinese Restaurant Process and other representations Read More »
This blog post is the fourth part of the series on Clustering with Dirichlet Process Mixture Models. In previous articles we discussed the Finite Dirichlet Mixture Models and we took the limit of their model for infinite k clusters which led us to the introduction of Dirichlet Processes. As we saw, our target is to
The Dirichlet Process Mixture Model Read More »
