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	Author	Allende, H.; Salas, R.; Moraga, C.
	Title	A robust and effective learning algorithm for feedforward neural networks based on the influence function			Type
	Year	2003	Publication	Lecture Notes in Computer Sciences	Abbreviated Journal	Lect. Notes Comput. Sc.
	Volume	2652	Issue		Pages	28-36
	Keywords	feedforward artificial neural networks; robust learning; effective parameter estimate
	Abstract	The learning process of the Feedforward Artificial Neural Networks relies on the data, though a robustness analysis of the parameter estimates of the model must be done due to the presence of outlying observations in the data. In this paper we seek the robust properties in the parameter estimates in the sense that the influence of aberrant observations or outliers in the estimate is bounded so the neural network is able to model the bulk of data. We also seek a trade off between robustness and efficiency under a Gaussian model. An adaptive learning procedure that seeks both aspects is developed. Finally we show some simulations results applied to the RESEX time series.
	Address	Univ Tecn Federico Santa Maria, Dept Informat, Valparaiso, Chile, Email: hallende@inf.utfsm.cl
	Corporate Author				Thesis
	Publisher	Springer-Verlag Berlin	Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN		Medium
	Area		Expedition		Conference	Pattern Recognition And Image Analysis
	Notes	WOS:000184832300004			Approved
	Call Number	UAI @ eduardo.moreno @			Serial	35
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