Artificial neural networks had their first heyday in molecular informatics and drug discovery
approximately two decades ago. Currently, we are witnessing renewed interest in adapting …

Feedforward neural networks trained by error backpropagation are examples of
nonparametric regression estimators. We present a tutorial on nonparametric inference and …

Many state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) Systems
are hybrids of neural networks and Hidden Markov Models (HMMs). Recently, more direct …

Though mathematical ideas underpin the study of neural networks, the author presents the
fundamentals without the full mathematical apparatus. All aspects of the field are tackled …

INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 1 Page 2
INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 3 Page 4 …

Designed as an introductory level textbook on Artificial Neural Networks at the postgraduate
and senior undergraduate levels in any branch of engineering, this self-contained and well …

Phantom auditory perception—tinnitus—is a symptom of many pathologies. Although there
are a number of theories postulating certain mechanisms of its generation, none have been …

Classification is one of the most active research and application areas of neural networks.
The literature is vast and growing. This paper summarizes some of the most important …

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 …

In this article, we perform an exhaustive analysis of the use of Artificial Neural Networks and
Deep Learning in the Visual Arts. We begin by introducing changes in Artificial Intelligence …