Gradient criterion method for neural networks and...

G - Physics – 06 – N

Patent

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G06N 3/08 (2006.01) G06Q 30/00 (2006.01)

Patent

CA 2403249

The present invention is drawn to a unique application of the Maximum Likelihood statistical method to commercial neural network technologies. The present invention utilizes the specific nature of the output in target marketing problems and makes it possible to produce more accurate and predictive results by minimizing a gradient criterion to produce model weights to get the maximum likelihood result. It is best used on "noisy" data and when one is interested in determining a distribution's overall accuracy, or best general description of reality.

La présente invention concerne une application unique de la méthode statistique du maximum de vraisemblance aux techniques des réseaux neuronaux commerciaux. La présente invention utilise la nature spécifique du résultat de problèmes de marketing ciblé, et permet la production de résultats prévisionnels plus précis par une minimisation d'un gradient visant à produire des pondérations de modèles permettant d'obtenir le résultat assorti du maximum de vraisemblance. Ce procédé s'utilise, de préférence, pour les données bruitées et lorsque l'on cherche à déterminer la précision générale d'une distribution, ou la meilleure description générale de la réalité.

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