In my last LTN blog post, I introduced the overall setting of my experiment. Before I can report on first results, I want and need to describe how we can evaluate the performance of the classifiers in this multi-label classification setting. This is what I’m going to do today.
I have already talked about multidimensional scaling (MDS) some time ago. Back then, I only gave a rough idea about what MDS does, but I didn’t really talk much about how MDS arrives at a solution. Today, I want to follow up on this and give you some intuition about what happens behind the scenes.
In previous blog posts I have already talked about Logic Tensor Networks in general, their relation to Conceptual Spaces, and several additional membership functions that are in line with the Conceptual Spaces framework. As I already mentioned before, I want to apply them in a “proof of concept” scenario. Today I’m going to sketch this scenario in more detail.
It has been quite some time since my last blog post (more than two months actually!) and the question is: What has happened?
Last time, I gave a rough outline of a hybrid approach for obtaining the dimensions of a conceptual space that uses both multidimensional scaling (MDS) and artificial neural networks (ANNs) . Today, I will show our first results (which we will present next week at the AIC workshop in Palermo).