@inproceedings{3c631ee5fe9446b0a50929f3ee835f6e,
title = "Generalised RBF networks trained using an IBL algorithm for mining symbolic data",
abstract = "The application of neural networks to domains involving pre- diction and classification of symbolic data requires a reconsideration and a careful definition of the concept of distance between patterns. Tra- ditional distances are inadequate to access the differences between the symbolic patterns. This work proposes the utilization of a statistically extracted distance measure in the context of Generalized Radial Basis Function (GRBF) networks. The main properties of the GRBF networks are retained in the new metric space. The regularization potential of these networks can be realized with this type of distance. Furthermore, the recent engineering of neural networks offers effective solutions for learning smooth functionals that lie on high dimensional spaces.",
author = "Liviu Vladutu and Stergios Papadimitriou and Severina Mavroudi and Anastasios Bezerianos",
year = "2001",
doi = "10.1007/3-540-45357-1_63",
language = "English (US)",
isbn = "3540419101",
series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",
publisher = "Springer Verlag",
pages = "587--598",
editor = "David Cheung and Williams, {Graham J.} and Qing Li",
booktitle = "Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings",
note = "5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 ; Conference date: 16-04-2001 Through 18-04-2001",
}