August
2007
Evaluation of an Integrated Multi-Task Machine Learning System with Humans in the Loop
Authors:
Abstract:
Performance of a cognitive personal assistant, RADAR, consisting of multiple machine learning components, natural language processing, and optimization was examined with a test explicitly developed to measure the impact of integrated machine learning when used by a human user in a real world setting. Three conditions (conventional tools, Radar without learning, and Radar with learning) were evaluated in a large-scale, between-subjects study. The study revealed that integrated machine learning does produce a positive impact on overall performance. This paper also discusses how specific machine learning components contributed to human-system performance.
@workshop{Steinfeld-2007-17040,
author = {Aaron Steinfeld And S. Rachael Bennett And Kyle Cunningham And Matt Lahut And Pablo-Alejandro Quinones And Django Wexler And Daniel Siewiorek And Jordan Hayes And Paul Cohen And Julie Fitzgerald And Othar Hansson And Mike Pool And Mark Drummond},
title = {Evaluation of an Integrated Multi-Task Machine Learning System with Humans in the Loop},
booktitle = {Proceedings of NIST Performance Metrics for Intelligent Systems Workshop (PerMIS '07)},
year = {2007},
month = {August},
}
author = {Aaron Steinfeld And S. Rachael Bennett And Kyle Cunningham And Matt Lahut And Pablo-Alejandro Quinones And Django Wexler And Daniel Siewiorek And Jordan Hayes And Paul Cohen And Julie Fitzgerald And Othar Hansson And Mike Pool And Mark Drummond},
title = {Evaluation of an Integrated Multi-Task Machine Learning System with Humans in the Loop},
booktitle = {Proceedings of NIST Performance Metrics for Intelligent Systems Workshop (PerMIS '07)},
year = {2007},
month = {August},
}