This is a preview. Log in through your library . Abstract After a hypothesis about some linear statistical model has been tested and rejected (e. g., in an ANOVA), many researchers employ the Scheffe ...

A robust non-parametric function fitting method is introduced. The estimate is motivated from the theory of M-estimation and of kernel estimation of regression functions. Consistency and asymptotic ...

Abstract: Activation functions are pivotal in neural networks, determining the output of each neuron. Traditionally, functions like sigmoid and ReLU have been static and deterministic. However, the ...

Recent studies have shown that the choice of activation function can significantly affect the performance of deep learning networks. However, the benefits of novel activation functions have been ...

Instituto de Ingeniería, Instituto de Investigaciones en Materiales,. Universidad Nacional Autónoma de México, México D.F., México. The purpose of this paper is to present a more rigorous derivation ...

A Bayesian Network uses the Bayes theorem to operate and provides a simple way of using the Bayes Theorem to solve complex problems. In contrast to other methodologies where probabilities are ...