Storing (Custom) Latex Theme
When using pandoc + beamer, I often store my theme file in the same folder. However, when you have ten slide decks, you end up with ten copies of the theme. Here, I describe where you should store these custom themes.
Python Generators
A Cheat Sheet.
Tags: python programming generator
Co-Embedding - Visualisation of a bipartite graph
Tags: Data Visualization Graph Embedding RecSys Clustering
A Gentle Introduction to Biometry.
Biometry is a very wide field - recognition methods, efficiency, security, privacy, ... There are so many sub-topics which are all important to consider when implementing a biometric system. Here, we will try to cover most of these topics, to give you starting clues to understand what is at stakes.
Tags: biometry template enrollment identification verification security
Making an Exam with a Static Website
Web Maze - Follow the Links Without Cheating
As a child, I found some books that you can play. You read one paragraph, and you are asked to take a decision - "Open the door" or "Ring the bell", "Go left" or "Go right". Then, depending on your choice, the story was different. Here, I describe a simple way to generate a static set of pages enabling to play the game.
Visualizing Syracuse Series
We naturally plot (x, y) on a plane, but what about complex data ? Series for instance. For this kind, traditional plot does not help. In this article, taking Syracuse series as an example, I describe how to process the data to get a nice 2D plot where some analysis can be performed.
Tags: machine learning graphs mathematics series Syracuse
Embedding Bokeh Plots with Jekyll
This website is generated thanks to Jekyll. Embedding a Bokeh plot as an html file was easy. However, this way prevents from adding multiple plot. In this article I describe one possible solution.
Tags: programming bokeh jekyll
Ensemble Clustering experiments.
Experiments against NMI evaluation.
Tags: machine learning unsupervised clustering ensemble
Ensemble Clustering Evaluation Issues.
EClust are often evaluated using NMI and ARI. If they were good measure, you would get a score of one for a perfect consensus, and 0 for a very bad one. There are very simple experiments showing they do not behave this way. This article will show the problem and describe where it comes from. Next, we will propose alternative measure this consensus functions.
Tags: clustering ensemble machine learning scoring unsupervised
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