OPUS: An Efficient Admissible Algorithm for Unordered Search
OPUS: An Efficient Admissible Algorithm for Unordered Search
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DOI：
10.1613/jair.227
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发表时间：
1995-06
期刊：
影响因子：
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通讯作者：
Geoffrey I. Webb
中科院分区：
文献类型：
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作者：
Geoffrey I. Webb
OPUS是一种分支和定界搜索算法，能够在搜索算子应用顺序不重要的空间中进行有效的容许搜索。该算法的搜索效率证明了非常大的机器学习搜索空间。可接受搜索的使用对机器学习社区具有潜在价值，因为它意味着可以精确地指定和操纵用于复杂学习任务的确切学习偏差。OPUS在人工智能的其他领域也有应用潜力，特别是真理维护。
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.