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Praca przedstawia struktury i algorytmy uczenia trzech podstawowych rodzajów sieci neuronowych: sieci sigmoidalnej wielowarstwowej MLP, sieci RBF oraz SVM. Sieci te pełnią podobną rolę uniwersalnego aproksymatora zmiennych wielowymiarowych, różniąc się przede wszystkim rodzajem zastosowanych neuronów i algorytmem uczącym. Pokazano uniwersalność tych rozwiązań i ich użyteczność w wielu problemach praktycznych...
The paper presents a comparative analysis of two of the most important neural network classifiers: the multilayer perceptron (MLP) and Support Vector Machine (SVM) in application to diagnostic problems. The structure as well as learning algorithms of both networks have been presented and compared. The results of numerical experiments comparing the performance of both classifiers on the artificial...
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