Abstract:Neural networks are routinely used for nonparametric regression modeling. The interest in these models is growing with ever-increasing complexities in modern datasets. With modern technological advancements, the number of predictors frequently exceeds the sample size in many application areas. Thus, selecting important predictors from the huge pool is an extremely important task for judicious inference. This paper proposes a novel flexible class of single-layer radial basis functions (RBF) networks. The propos… Show more
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