Abstract. Probabilistic Neural Networks (PNNs) constitute a promising methodology for classification and prediction tasks. Their performance depends heavily on several factors, su...
Vasileios L. Georgiou, Sonia Malefaki, Konstantino...
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
This paper presents an approach to the joint optimization of neural network structure and weights which can take advantage of backpropagation as a specialized decoder. The approach...
— The optimization of classification systems is often confronted by the solution over-fit problem. Solution over-fit occurs when the optimized classifier memorizes the traini...
Paulo Vinicius Wolski Radtke, Tony Wong, Robert Sa...
In the eld of arti cial evolution creating methods to evolve neural networks is an important goal. But how to encode the structure and properties of the neural network in the geno...