Neural Networks for Surface Reconstruction
Enachescu Calin, László Barna Iantovics, László Barna Iantovics, Enachescu Calin, Florin Gheorghe Filip · AIP conference proceedings · 2009
Solving a problem with a neural network a primordial task is establishing the network topology. Generally neural network topology determination is a complex problem and cannot be easily solved. When the number of trainable layers and processor units is too low, the network is not able to learn the proposed problem. When the number of layers and neurons is too high, the learning process becomes too slow. Learning from examples means being able to infer the functional dependence between input and output spaces X and Z, given the knowledge of the set of examples T. It means that, after we have “learned” N examples, when a new input variable x comes in, we need to be able to estimate, according to some criterion that we will specify, a corresponding value of z. From this point of view learning is equivalent to a function approximation.