Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Ensemble Construction via Designed Output Distortion
Ist Teil von
Lecture notes in computer science, 2003, p.286-295
Ort / Verlag
Berlin, Heidelberg: Springer Berlin Heidelberg
Erscheinungsjahr
2003
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
A new technique for generating regression ensembles is introduced in the present paper. The technique is based on earlier work on promoting model diversity through injection of noise into the outputs; it differs from the earlier methods in its rigorous requirement that the mean displacements applied to any data points output value be exactly zero.
It is illustrated how even the introduction of extremely large displacements may lead to prediction accuracy superior to that achieved by bagging.
It is demonstrated how ensembles of models with very high bias may have much better prediction accuracy than single models of the same bias-defying the conventional belief that ensembling high bias models is not purposeful.
Finally is outlined how the technique may be applied to classification.