This model can easily be augmented to thousands of possible layers without loss of predictive power, and has the potential to overcome our difficulties simultaneously in building a model that has a good fit on the test data, and don't overfit. The classical Multilayer Feedforward model has been re-considered and a novel $N_k$-architecture is proposed to fit any multivariate regression task. The tools are given through the chapters that contain our developments. ![]() ![]() We have performed in this thesis many experiments that validate this concept in many ways. We mean a learning model that can be generalized, and moreover, that can always fit perfectly the test data, as well as the training data.
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