Abstract:
In this thesis we have studied how a technique based on how game theory can improve classification
results obtained with a deep learning module.
In order to get this improvement we have applied this method to the music genre classification problem,
comparing the obtained results.
The proposed model is composed by a convolutional recurrent neural network (CRNN), that deals with
classifying every single element, and the Graph Transduction Game (GTG) method, that allows to compare
these elements on the basis of a similarity measure and thus exploit the contextual information
in order to get a better classification.
The idea behind this work is that the neural network architecture does not directly exploit
the information coming from the comparison of the observations passed as input. Therefore
we think that the introduction of a module in charge for this purpose can improve final accuracy,
especially when we work with limited datasets.
In order to assess the effect of the proposed approach we have performed experiments on benchmark datasets
and we report the results obtained.
