Reliable agnostic learning

It is well known that in many applications erroneous predictions of one type or another must be avoided. In some applications, like spam detection, false positive errors are serious problems. In other applications, like medical diagnosis, abstaining from making a prediction may be more desirable tha...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Journal of computer and system sciences 2012-09, Vol.78 (5), p.1481-1495
Hauptverfasser:	Kalai, Adam Tauman, Kanade, Varun, Mansour, Yishay
Format:	Artikel
Sprache:	eng
Schlagworte:	Agnostic learning Algorithms Classification Classifiers Computer simulation Error detection Learning Mathematical analysis Mathematical models PAC learning Queries
Online-Zugang:	Volltext
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