Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  algorithms of neural-network type are introduced for solving estimation and classification problems when assumptions about independence, Gaussianity, and stationarity of the observation samples are no longer valid. Specifically, the asymptotic normality of several nonparametric classification tests is demonstrated and their implementation using a neural-network approach is presented. Initially, the neural nets train themselves via learning samples for nominal noise and alternative hypotheses distributions resulting in near optimum performance in a particular stochastic environment. In other than the nominal environments, hover, high efficiency is maintained by adapting the optimum nonlinearities to changing conditions during operation via parallel networks, without disturbing the classification process. Furthermore, the superiority in performance of the proposed networks over more traditional neural nets is demonstrated in an application involving pattern recognition.
['Evriclea Voudouri-Maniati', 'Ludwik Kurz', 'John M. Kowalski']
I am writing a research paper on a idea A neural-network approach to nonparametric and robust classification procedures
[{"from": "human", "value": "I am writing a research paper on a idea A neural-network approach to nonparametric and robust classification procedures"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  algorithms of neural-network type are introduced for solving estimation and classification problems when assumptions about independence, Gaussianity, and stationarity of the observation samples are no longer valid. Specifically, the asymptotic normality of several nonparametric classification tests is demonstrated and their implementation using a neural-network approach is presented. Initially, the neural nets train themselves via learning samples for nominal noise and alternative hypotheses distributions resulting in near optimum performance in a particular stochastic environment. In other than the nominal environments, hover, high efficiency is maintained by adapting the optimum nonlinearities to changing conditions during operation via parallel networks, without disturbing the classification process. Furthermore, the superiority in performance of the proposed networks over more traditional neural nets is demonstrated in an application involving pattern recognition."}]