There was a chaos theory related question on data mining here: What are the practical applications of chaos theory in data mining?, but it was deemed too broad. I'm going to try to tackle this topic in a more focused and terse way.
Models rooted in chaos theory have helped scientists understand complex systems. Some cool examples are: robotics, cryptography, and bird migrations. One of the chief characteristics is the sensitivity to initial conditions. While I am not an expert in either field, but I see certain similarities between the two. For instance, certain machine learning techniques like cluster analysis also have initial conditions (the initial cluster centers). Chaos theory and machine learning also are used for predictive analysis.
I concede it's not the most elegant comparison; there are many differences between the two. Now don't quote me on this, but I think of chaos theory as a kind of blue-print for modeling complex systems, and I think of machine learning as a tool for optimizing and best utilizing high dimensional data. In machine learning the model is only part of the ML block diagram. It could be a regression model, clustering model or something else entirely. So again, it's not fair to compare them in all aspects. With this disclaimer out of the way, here is my question:
Is there a particular field of machine learning that is devoted to applying chaos theory models, or are researchers merely cherry-picking a few models from chaos theory? Or do machine learning researchers stand to gain little from chaos theory since they can go with atheoretical approaches and toss theory out the window and just use a multitude of features?