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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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