Text classification is a common machine learning task which is known in various contexts as sentiment analysis, language detection, and category tagging. Many standard AI tools can be used on text given an appropriate feature selection function, which essentially transforms text down into a high-dimensional vector. However there are also certain techniques that work directly on the text, and this article is about a couple of those techniques that are enabled and demonstrated by the new release of the CharTrie component of the SimiaCryptus utilities library.
I discussed in my previous blog post a distributed software transactional memory library I was implementing in Scala. Being a platform, it is hard to demonstrate without some interesting application running on it. I have thus created a decision tree service - a RESTful api to populate, train, and query decision tree structures. As the majority of this post will discuss the decision tree service, I would like to emphasize that this is merely a demo application for a research-grade platform.
Happy Fathers Day! I’ve just finished up a review of the next project in my queue: Volumetry. The readme on github has a bunch of pretty pictures now and should be helpful to anyone interested in the research. If it looks interesting, I encourage you to run the code yourself; the 2d images aren’t nearly as interesting as the 3d models. Included below are some snippets from the new documentation:
NOTE: This article is only a draft, but I wanted to make sure it finally gets published in some form. Revisions and improvements (e.g. illustrations) to come. In this article I will briefly summarize a research project I have been playing with off and on for about a year. My goal is to provide a workable introduction to my research project and a brief discussion of the theory and rationale.