Learning from Data by Neural Networks with a Limited Complexity

0405074 - UIVT-O 20030188 RIV IT eng C - Conference Paper (international conference)
Kůrková, Věra - Sanguineti, M.
Learning from Data by Neural Networks with a Limited Complexity.
Artificial Neural Networks in Pattern Recognition. Florence: University of Florence, 2003 - (Gori, M.; Marinai, S.), s. 146-151
[IAPR TC3 Workshop /1./. Florence (IT), 12.09.2003-13.09.2003]
R&D Projects: GA ČR GA201/02/0428
Grant - others:IT-CZ Area MC6(XX) Project 22
Institutional research plan: AV0Z1030915
Keywords : learning from data * neural networks * kernel methods
Subject RIV: BA - General Mathematics

Learning from data formalized as a minimization of a relularized empirical error is studied in terms of approximate minimization over sets of functions computable by networks with increasing number of hidden units. There are derived upper bounds on speed of convergence of infima achievable over networks with n hidden inits to the global infimum. The bounds are expressed in terms of norms tailored to the type of network units and moduli of continuity of regularized empirical error functionals.
Permanent Link: http://hdl.handle.net/11104/0125289