We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a...
This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The iss...
Vladimir Cherkassky, Vladimir M. Krasnopolsky, Dim...
A general method to design optimal redundant sensor network even in the case of one sensor failure and able to estimate process key parameters within a required accuracy is propos...
Decision making is tightly related to the understanding of the design and manufacturing practices. In our previous work, we proposed an intuitive approach for geometric modeling in...
Ioana Ciuciu, Robert Meersman, Estelle Perrin, Fr&...
This work presents a novel approach in automatic detection of the lung nodules and is compared with respect to parametric nodule models in terms of sensitivity and specificity. A ...
Amal A. Farag, James Graham, Salwa Elshazly, Aly F...