Bayesian MLP neural networks are a flexible tool in complex nonlinear problems. The approach is complicated by need to evaluate integrals over high-dimensional probability distri...
The main purpose of this paper is to compare the support vector machine (SVM) developed by Vapnik with other techniques such as Backpropagation and Radial Basis Function (RBF) Net...
It is often reported in the technique literature that the success of the self-organizing feature map (SOM) formation is critically dependent on the initial weights and the selectio...
We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the def...
: The main purpose of this work is to create a physiologically realistic computational model of the retina by providing a flexible, real-valued three-dimensional architecture. This...
Michael Tadross, Cameron Whitehouse, Melissa Horns...
KBCC is an extension of the cascade-correlation algorithm that treats functions encapsulating prior knowledge as black-boxes which, like simple sigmoidal neurons, can be recruited...
The problem of designing input signals for optimal generalization in supervised learning is called active learning. In many active learning methods devised so far, the bias of the...