Doubly stochastic Poisson processes in artificial neural learning

1998 ◽  
Vol 9 (1) ◽  
pp. 229-231 ◽  
Author(s):  
H.C. Card
Keyword(s):  
Poisson Processes ◽  
Doubly Stochastic ◽  
Neural Learning ◽  
Artificial Neural

Prediction of the Intensity Process of Doubly Stochastic Multichannel Poisson Processes

2012 ◽  
Vol 57 (7) ◽  
pp. 1843-1848
Author(s):  
Rosa María Fernandez-Alcala ◽  
Jesús Navarro-Moreno ◽  
Juan Carlos Ruiz-Molina
Keyword(s):  
Poisson Processes ◽  
Doubly Stochastic ◽  
Intensity Process

Supervised Learning with Artificial Neural Networks

2008 ◽  
pp. 24-40
Author(s):  
Darryl Charles ◽  
Colin Fyfe ◽  
Daniel Livingstone ◽  
Stephen McGlinchey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Theory And Practice ◽  
Delta Rule ◽  
Neural Learning ◽  
Artificial Neural

In this chapter we will look at supervised learning in more detail, beginning with one of the simplest (and earliest) supervised neural learning algorithms – the Delta Rule. The objectives of this chapter are to provide a solid grounding in the theory and practice of problem solving with artificial neural networks – and an appreciation of some of the challenges and practicalities involved in their use.

Learning in Feed-Forward Artificial Neural Networks I

2011 ◽  
pp. 1004-1011
Author(s):  
Lluís A. Belanche Muñoz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Systems ◽  
Error Function ◽  
Single Layer ◽  
Departure Point ◽  
Feed Forward ◽  
Delta Rule ◽  
Neural Learning ◽  
Artificial Neural

The view of artificial neural networks as adaptive systems has lead to the development of ad-hoc generic procedures known as learning rules. The first of these is the Perceptron Rule (Rosenblatt, 1962), useful for single layer feed-forward networks and linearly separable problems. Its simplicity and beauty, and the existence of a convergence theorem made it a basic departure point in neural learning algorithms. This algorithm is a particular case of the Widrow-Hoff or delta rule (Widrow & Hoff, 1960), applicable to continuous networks with no hidden layers with an error function that is quadratic in the parameters.

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