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Nhan đề : 
Algorithm selection on a meta level
Tác giả : 
Alexander, Tornede
Lukas, Gehring
Tanja, Tornede
Năm xuất bản : 
2023
Nhà xuất bản : 
Springer
Tóm tắt : 
The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors.
Mô tả: 
CC BY
URI: 
https://link.springer.com/article/10.1007/s10994-022-06161-4
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7345
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