Title :
Fast inner-outer point evaluation in a polytopic generalization domain
Author :
Agamennoni, Osvaldo ; Mandolesi, Pablo S.
Author_Institution :
Dept. de Ingenieria Electrica, Univ. Nacional del Sur, Bahia Blanca, Argentina
Abstract :
In this paper the generalization domain or applicability domain of a given model is addressed. The generalization domain is related with the interpolation domain that is usually defined through the polytope formed by the training data. Some considerations about the relationship between the interpolation and the generalization domains are given. A fast algorithm to test in real time if a model input is inside or outside the interpolation domain is presented. This is a more general problem commonly encountered in many areas, i.e., to check if a point is inside or not a given polytope. An example to evaluate points of a sphere from a closed ball is discussed to show the performance of the algorithm
Keywords :
computational geometry; feedforward neural nets; generalisation (artificial intelligence); interpolation; optimisation; real-time systems; feedforward neural network; inner-outer point evaluation; interpolation; polytope; polytopic generalization domain; real time systems; Clustering algorithms; Density functional theory; Electronic mail; Extrapolation; Interpolation; Neural networks; Partitioning algorithms; Recurrent neural networks; Testing; Training data;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.614123