OPUS: An Efficient Admissible Algorithm For Unordered Search
G. Webb. Journal of Artificial Intelligence Research, (1995)
Abstract
OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm's search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance.
%0 Journal Article
%1 Webb95
%A Webb, G. I.
%C Menlo Park, CA
%D 1995
%I AAAI Press
%J Journal of Artificial Intelligence Research
%K Association Discovery Learning, OPUS, Rule Search,
%P 431-465
%T OPUS: An Efficient Admissible Algorithm For Unordered Search
%U http://dx.doi.org/10.1613/jair.227
%V 3
%X OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm's search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance.
@article{Webb95,
abstract = {OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm's search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance.},
added-at = {2016-03-20T05:42:04.000+0100},
address = {Menlo Park, CA},
audit-trail = {Link to paper via JAIR website},
author = {Webb, G. I.},
biburl = {https://www.bibsonomy.org/bibtex/2d990736121d581111c570a24d58791d1/giwebb},
interhash = {a99f620a328e8f5e98b0b81de3ce6db1},
intrahash = {d990736121d581111c570a24d58791d1},
journal = {Journal of Artificial Intelligence Research},
keywords = {Association Discovery Learning, OPUS, Rule Search,},
pages = {431-465},
publisher = {AAAI Press},
timestamp = {2016-03-20T05:42:04.000+0100},
title = {OPUS: An Efficient Admissible Algorithm For Unordered Search},
url = {http://dx.doi.org/10.1613/jair.227},
volume = 3,
year = 1995
}