%0 Conference Paper %1 ASTC_2004_Getting_It_Right_from_the_Very_Start %A Francone, Frank D. %A Deschaine, Larry M. %B 2004 Business and Industry Symposium %C Washington, DC %D 2004 %K Environmental Science, algorithms, anomaly detection, expert genetic geophysics, information learning, machine programming, systems theory, underground %T Getting It Right at the Very Start -- Building Project Models where Data Is Expensive by Combining Human Expertise, Machine Learning and Information Theory %U http://www.scs.org/docInfo.cfm?get=1720 %X Building models using machine learning techniques requires data. For some projects, gathering data is very expensive. In this type of project, there are two significant costs to using machine learning techniques in this type of project: (1) Machine learning models cannot even begin to make predictions until the project has already spent a lot of money gathering data; and (2) While the data is being gathered to train the machine learning system, unnecessary costs are incurred in making inefficient decisions. Engineers may address this type of problem efficiently when enough human expertise exists about the problem domain to be modelled. This work proposes an approach to combining human expertise, machine learning and information theory that makes efficient and effective decisions from the start of the project, while project data is being gathered.

@inproceedings{ASTC_2004_Getting_It_Right_from_the_Very_Start, abstract = {Building models using machine learning techniques requires data. For some projects, gathering data is very expensive. In this type of project, there are two significant costs to using machine learning techniques in this type of project: (1) Machine learning models cannot even begin to make predictions until the project has already spent a lot of money gathering data; and (2) While the data is being gathered to train the machine learning system, unnecessary costs are incurred in making inefficient decisions. Engineers may address this type of problem efficiently when enough human expertise exists about the problem domain to be modelled. This work proposes an approach to combining human expertise, machine learning and information theory that makes efficient and effective decisions from the start of the project, while project data is being gathered.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Washington, DC}, author = {Francone, Frank D. and Deschaine, Larry M.}, biburl = {https://www.bibsonomy.org/bibtex/202f3e3c493f01d554bed64ffba378bb3/brazovayeye}, booktitle = {2004 Business and Industry Symposium}, interhash = {8e57db90e0a65e9644d466cebed8b7e5}, intrahash = {02f3e3c493f01d554bed64ffba378bb3}, keywords = {Environmental Science, algorithms, anomaly detection, expert genetic geophysics, information learning, machine programming, systems theory, underground}, month = {April}, organisation = {Society for Modeling and Simulation}, size = {7 pages}, timestamp = {2008-06-19T17:39:46.000+0200}, title = {Getting It Right at the Very Start -- Building Project Models where Data Is Expensive by Combining Human Expertise, Machine Learning and Information Theory}, url = {http://www.scs.org/docInfo.cfm?get=1720}, year = 2004 }