Applying Logic Tensor Networks (Part 2)

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.

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How does multidimensional scaling work?

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.

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Applying Logic Tensor Networks (Part 1)

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.

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A hybrid way for obtaining the dimensions of a conceptual space (Part 2)

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) [1]. Today, I will show our first results (which we will present next week at the AIC workshop in Palermo).

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