Getting It Right at the Very Start -- Building Project Models where Data Is Expensive by Combining Human Expertise, Machine Learning and Information Theory
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{ASTC_2004_Getting_It_Right_from_the_Very_Start,
-
author = "Frank D. Francone and Larry M. Deschaine",
-
title = "Getting It Right at the Very Start -- Building Project
Models where Data Is Expensive by Combining Human
Expertise, Machine Learning and Information Theory",
-
booktitle = "2004 Business and Industry Symposium",
-
year = "2004",
-
address = "Washington, DC",
-
month = apr,
-
organisation = "Society for Modeling and Simulation",
-
keywords = "genetic algorithms, genetic programming, Environmental
Science, geophysics, information theory, underground
anomaly detection, machine learning, expert systems",
-
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/ASTC_2004_Getting_It_Right_from_the_Very_Start.pdf",
-
URL = "http://www.scs.org/docInfo.cfm?get=1720",
-
size = "7 pages",
-
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.",
-
notes = "
",
- }
Genetic Programming entries for
Frank D Francone
Larry M Deschaine
Citations