DocumentCode
343512
Title
Gradient based adaptive regularization
Author
Eigenmann, Robert ; Nossek, Josef A.
Author_Institution
Inst. for Network Theory & Circuit Design, Munchen Univ., Germany
fYear
1999
fDate
36373
Firstpage
87
Lastpage
94
Abstract
A technique to optimize regularization parameters for a given supervised training problem is presented. A training database is applied to minimize a regularized cost function, and a validation database is used to estimate and optimize generalization properties by means of a modification of regularization. The performance is validated for a vowel classification task and compared to other approaches
Keywords
feedforward neural nets; learning (artificial intelligence); multilayer perceptrons; pattern classification; speech recognition; generalization properties; gradient based adaptive regularization; regularization parameters; regularized cost function; supervised training problem; training database; validation database; vowel classification task; Circuit synthesis; Cost function; Data structures; Databases; Error correction; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.
Conference_Location
Madison, WI
Print_ISBN
0-7803-5673-X
Type
conf
DOI
10.1109/NNSP.1999.788126
Filename
788126
Link To Document