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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... (Update)
Context of citations to this paper: More
...and selects the beam width dynamically. DALI mainly differs from LFC on two steps: 1. A systematic search to avoid redundant combinations [22, 27], Lines 3 4, Fig. 5. Each monomial F i conjoins several boolean features (or their complements) e.g. F i = x 1 x 3 x 5 . Because...
.... Systematic search expands the children of search nodes in a manner that ensures that no node can ever be generated more than once [9 12, 14]. Because non redundant expansion is achieved without access to large, rapidly changing data structures, such as lists of open and closed...
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BibTeX entry: (Update)
Geoffrey I. Webb. OPUS: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research, 3:45--83, 1996. http://citeseer.ist.psu.edu/article/webb95opus.html More
@article{ webb95opus,
author = "Geoffrey I. Webb",
title = "{OPUS}: An Efficient Admissible Algorithm for Unordered Search",
journal = "Journal of Artificial Intelligence Research",
volume = "3",
pages = "431-465",
year = "1995",
url = "citeseer.ist.psu.edu/article/webb95opus.html" }
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