Extensions of a Theory of Networks for Approximation and Learning: Outliers and Negative Examples

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6530

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DC Field	Value	Language
dc.creator	Girosi, Federico	-
dc.creator	Poggio, Tomaso	-
dc.creator	Caprile, Bruno	-
dc.date	2004-10-04T15:14:46Z	-
dc.date	2004-10-04T15:14:46Z	-
dc.date	1990-07-01	-
dc.date.accessioned	2013-10-09T02:45:55Z	-
dc.date.available	2013-10-09T02:45:55Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1220	-
dc.identifier	http://hdl.handle.net/1721.1/6530	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multi-dimensional function. From this point of view, this form of learning is closely related to regularization theory. In this note, we extend the theory by introducing ways of dealing with two aspects of learning: learning in the presence of unreliable examples and learning from positive and negative examples. The first extension corresponds to dealing with outliers among the sparse data. The second one corresponds to exploiting information about points or regions in the range of the function that are forbidden.	-
dc.format	3388253 bytes	-
dc.format	1212626 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1220	-
dc.title	Extensions of a Theory of Networks for Approximation and Learning: Outliers and Negative Examples	-
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