All posts in: RMSProp


Online demo of t-SNE visualization you can see here.
Machine learning algorithms have been put to good use in various areas for several years already. Analysis of various political events can become one of such areas. For instance, it can be used for predicting voting results, developing mechanisms for clustering the decisions made, analysis of political actors’ actions. In this article, I will try to describe the result of a research in this area.

Problem Definition  

Modern machine learning capabilities allow converting and visualizing huge amounts of data. Thereby it became possible to analyze political parties’ activities by converting voting instances that took place during 4 years into a self-organizing space of points that reflects actions of each elected official.

Each politician expressed themselves via 12 000 voting instances. Each voting instance can represent one of five possible actions (the person was absent, skipped the voting, voted approval, voted in negative, abstained).

The task is to convert the results of all voting instances into a point in the 3D Euclidean space that will reflect some considered attitude.

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This post is the first one in the series of posts where I will describe the creation of a neural network, modelling the behaviour of a small organism. Why model such a system, what functions it will perform and what the results I will get will be described in the next posts.

The only thing I want to describe in this post is the solution which helps accelerate the training of neural networks.

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