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 […]
This article is the fifth part of the tutorial on Clustering with DPMM. In the previous posts we covered in detail the theoretical background of the method and we described its mathematical representationsmu and ways to construct it. In this post we will try to link the theory with the practice by introducing two models […]
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 […]
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 […]
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 […]
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 […]
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 […]
In the previous article we have discussed about the Data Envelopment Analysis technique and we have seen how it can be used as an effective non-parametric ranking algorithm. In this blog post we will develop an implementation of Data Envelopment Analysis in JAVA and we will use it to evaluate the Social Media Popularity of […]
Data Envelopment Analysis, also known as DEA, is a non-parametric method for performing frontier analysis. It uses linear programming to estimate the efficiency of multiple decision-making units and it is commonly used in production, management and economics. The technique was first proposed by Charnes, Cooper and Rhodes in 1978 and since then it became a […]
In this article we will discuss how you can build easily a simple Facebook Sentiment Analysis tool capable of classifying public posts (both from users and from pages) as positive, negative and neutral. We are going to use Facebook’s Graph API Search and the Datumbox API 1.0v. Similar to the Twitter Sentiment Analysis tool that […]
In previous articles we have discussed the theoretical background of Naive Bayes Text Classifier and the importance of using Feature Selection techniques in Text Classification. In this article, we are going to put everything together and build a simple implementation of the Naive Bayes text classification algorithm in JAVA. The code of the classifier is […]
Recently, I have been refreshing my knowledge of Machine Learning by taking Andrew Ng’s excellent Stanford Machine Learning course online. The lecture module on Neural Networks ends with an intriging motivating video of the ALVINN autonomous car driving itself along normal roads at CMU in the mid 90s.
<p><em>This is an archive of Dan Weinreb's comments on Symbolics and Lisp machines.</em></p> <h3 id="rebuttal-to-stallman-s-story-about-the-formation-of-symbolics-and-lmi">Rebuttal to Stallman’s Story About The Formation of Symbolics and LMI</h3> <p>Richard Stallman has been telling a story about the origins of the Lisp machine companies, and the effects on the M.I.T. Artificial Intelligence Lab, for many years. He has published it in a book, and in a widely-referenced paper, which you can find
