Often researchers find parametric models restrictive and sensitive to deviations from the
parametric specifications; semi-nonparametric models are more flexible and robust, but lead …

We are concerned with optimization of macroscale elastic structures that are designed
utilizing spatially varying microscale metamaterials. The macroscale optimization is …

At the heart of deep learning we aim to use neural networks as function approximators-
training them to produce outputs from inputs in emulation of a ground truth function or data …

This book provides the first comprehensive treatment of feed-forward neural networks from
the perspective of statistical pattern recognition. After introducing the basic concepts of …

This book is an ambitious effort by three well-known and well-respected scholars to fill an
acknowledged void in the literature—a text covering the burgeoning field of empirical …

We show that standard multilayer feedforward networks with as few as a single hidden layer
and arbitrary bounded and nonconstant activation function are universal approximators with …

We give conditions ensuring that multilayer feedforward networks with as few as a single
hidden layer and an appropriately smooth hidden layer activation function are capable of …

In this survey we discuss various approximation-theoretic problems that arise in the
multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of …

In this study, we investigate the relationship between tax avoidance and earnings
management in the largest five European Union economies by using artificial neural …

Neural networks provide a range of powerful new techniques for solving problems in pattern
recognition, data analysis, and control. They have several notable features including high …