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• W2074026175 abstract "Classification problems in critical applications such as health care or security often require very high reliability because of the high costs of errors. In order to achieve this reliability, such systems often require the use of sequential inspections, where additional data can be collected to resolve ambiguous test cases. It is impractical or costly to collect this additional data on every sample, so one must find identify a policy that selects which samples need further examination. In this paper, we present a theory for designing support vector machine classifiers that include the option to delay decision and collect further information. We present a convex programming formulation for training such classifiers, and define a fast coordinate ascent algorithm to solve the dual of this optimization problem. The performance of the resulting classifiers is evaluated on a test suite involving detection of malignancies in hyperspectral measurements of colon polyps collected during colonoscopies." @default.
• W2074026175 created "2016-06-24" @default.
• W2074026175 creator A5043142375 @default.
• W2074026175 creator A5058386500 @default.
• W2074026175 date "2009-12-01" @default.
• W2074026175 modified "2023-10-17" @default.
• W2074026175 title "Support vector machine classifiers for sequential decision problems" @default.
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• W2074026175 doi "https://doi.org/10.1109/cdc.2009.5400391" @default.
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