Robust modeling

Details Robust modeling Robust modeling

2011-06-07

2011-06-07

Starks, Jr., Wilbert L (Department: 2129)

Data processing: artificial intelligence

Machine learning

C706S020000, C706S045000

Reissue Patent

active

RE042440

ABSTRACT:
A system and method are disclosed for generating a robust model of a system that selects a modeling function. The modeling function has a set of weights and the modeling function has a complexity that is determined by a complexity parameter. For each of a plurality of values of the complexity parameter an associated set of weights of the modeling function is determined such that a training error is minimized for a training data set. An error for a cross validation data set is determined for each set of weights associated with one of the plurality of values of the complexity parameter and the set of weights associated with the value of the complexity parameter is selected that best satisfies a cross validation criteria. Thus, the selected set of weights used with the modeling function provides the robust model.

REFERENCES:
patent: 5592589 (1997-01-01), Poon
patent: 5640492 (1997-06-01), Cortes et al.
patent: 5649068 (1997-07-01), Boser et al.
patent: 5659667 (1997-08-01), Buescher et al.
patent: 5684929 (1997-11-01), Cortes et al.
patent: 5720003 (1998-02-01), Chiang et al.
patent: 5745383 (1998-04-01), Barber
patent: 5819247 (1998-10-01), Freund et al.
patent: 5987444 (1999-11-01), Lo
patent: 6393413 (2002-05-01), Jorgensen et al.
patent: 6714925 (2004-03-01), Barnhill et al.
patent: 6760715 (2004-07-01), Barnhill et al.
patent: 7072841 (2006-07-01), Pednault
Joel Ratsaby et al., Towards Robust Model Selection Using Estimation and Approximation Error Bounds, 1996, ACM, 57-67.
Tan, Y., et al., A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik-Chervonenkis Dimension, IEEE Transactions on knowledge and Data Engineering, vol. 16., No. 4, Apr. 2004, pp. 385-395.
Joel Ratsaby et al; Towards Robust Model Selection Using Estimation and Approximation Error Bounds; 1996; ACM; 0-89791-811-8/96/06; 57-67.
Richard P. Brent; Fast Training Algorithms for Multilayer Neural Nets; 1991; IEEE; 1045-9227/91/0500-0346; 346-354.
Vladimir Cherkassky et al; Regularization Effect of Weight Initialization in Back Propagation Networks; 1998; IEEE; 0-7803-4859-1/98; 2258-2261.
W.H. Highleyman, “The Design and Analysis of Pattern Recognition Experiments”, The Bell System Technical Journal, Mar. 1962, pp. 723-744.
I. Guyon, V. Vapnik, B. Boser, L. Bottou and S.A. Solla, “Structural Risk Minimization for Character Recognition”, Advances in Neural Information Processing Systems 4, Morgan Kaufmann 1992, pp. 471-479.
I. Guyon, V. Vapnik, B. Boser, L. Bottou and S.A. Solla: Capacity control in linear classifiers for pattern recognition, Proceedings of the 11th IAPR International Conference on Pattern Recognition, Conference B: Pattern Recognition Methodology and Systems , II:385-388, IEEE, Sep. 1992.
“La Théorie Statistique de Vladimir N. Vapnik”, Fiche de Référence, Conférence Nouvelles méthodes en statistiques appliquées—Application aux problèmes de prévision, May 6, 1994, Neuristique S.A., Paris.
Paul J. Werbos, “Backpropagation: Basics and New Developments”, The Handbook of Brain Theory and Neural Networks, Michael Arbib editor, MIT Press, 1995, pp. 134-139.
Michael P. Perrone, “Averaging/Modular Techniques for Neural Networks”, The Handbook of Brain Theory and Neural Networks, Michael Arbib editor, MIT Press, 1995, pp. 126-129.
Grace Wahba, “Generalization and Regularization in Nonlinear Learning Systems”, The Handbook of Brain Theory and Neural Networks, Michael Arbib editor, MIT Press, 1995, pp. 426-430.
Vladimir N. Vapnik, “Controlling the Generalization Ability of Learning Processes”, The Nature of Statistical Learning Theory, chapter 4, Springer-Verlag, New-York, 1995, pp. 89-118.
Isabelle Guyon, “A Scaling Law for the Validation-Set Training-Set Size Ratio”, AT&T Bell Laboratories, 1997.
Léon Bottou, “La Mise en Oeuvre des Idées de Vladimir N. Vapnik”, Statistiques et Méthodes Neuronales, chapter 16, Dunod, Paris, 1997, pp. 262-274.

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