On-the-Job Learning with Bayesian Decision Theory

Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an "on-the-job" setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the...

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Veröffentlicht in:	arXiv.org 2015-12
Hauptverfasser:	Werling, Keenon, Chaganty, Arun, Liang, Percy, Manning, Chris
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Annotations Bayesian analysis Computer simulation Computing costs Crowdsourcing Decision theory Distance learning Human performance Image classification Machine learning Object recognition
Online-Zugang:	Volltext
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