Li et al., 2018 - Google Patents
Data-driven ranking and selection: High-dimensional covariates and general dependenceLi et al., 2018
- Document ID
- 14276442028815480161
- Author
- Li X
- Zhang X
- Zheng Z
- Publication year
- Publication venue
- 2018 Winter Simulation Conference (WSC)
External Links
Snippet
This paper considers the problem of ranking and selection with covariates and aims to identify a decision rule that stipulates the best alternative as a function of the observable covariates. We propose a general data-driven framework to accommodate (i) high …
- 238000000034 method 0 abstract description 44
Classifications
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- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
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- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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- G06K9/6228—Selecting the most significant subset of features
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