This blog post closes the “A Hybrid Way: Reloaded” mini-series. So far, I have analyzed the MDS solutions in part 1 and investigated first regression results in part 2 (with respect to the effects of feature space, correct vs. shuffled targets, and regularization). Today, I want to analyze what happens if we use different MDS algorithms for constructing the similarity spaces and to what extent our regression results depend on the number of dimensions in the similarity space.
A Hybrid Way: Reloaded (Part 2)
In my last blog post, I analyzed the differences of metric vs. nonmetric MDS when applied to the NOUN data base. Today, I want to continue with showing some machine learning results, updating the ones from our 2018 AIC paper (see these two blog posts: part 1 and part 2).
A Hybrid Way: Reloaded (Part 1)
Applying Logic Tensor Networks (Part 5)
Last time, I have shared the first results obtained by the LTN on the conceptual space of movies. Today, I want to give you a quick update on the first membership function variant that I have investigated.
Applying Logic Tensor Networks (Part 4)
After having already written a lot about Logic Tensor Networks, today I will finally share some first results of how they perform in a multi-label classification task on the conceptual space of movies.