Neural networks
An overview of current research on artificial neural networks is presented, emphasizing a statistical perspective, that views neural networks as parameterized graphs that make probabilistic assumptions about data and learning algorithms as methods for finding parameter values that look probable in the light of the data.
Abstract
We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view learning algorithms as methods for finding parameter values that look probable in the light of the data. We discuss basic issues in representation and learning, and treat some of the practical issues that arise in fitting networks to data. We also discuss links between neural networks and the general formalism of graphical models.