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R-LAIR: Riverside Lab for Artificial Intelligence Research

Chained Boosting (2007)

by Christian R. Shelton, Wesley Huie, and Kin Fai Kan

Abstract: We describe a method to learn to make sequential stopping decisions, such as those made along a processing pipeline. We envision a scenario in which a series of decisions must be made as to whether to continue to process. Further processing costs time and resources, but may add value. Our goal is to create, based on historic data, a series of decision rules (one at each stage in the pipeline) that decide, based on information gathered up to that point, whether to continue processing the part. We demonstrate how our framework encompasses problems from manufacturing to vision processing. We derive a quadratic (in the number of decisions) bound on testing performance and provide empirical results on object detection.

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Christian R. Shelton, Wesley Huie, and Kin Fai Kan (2007). "Chained Boosting." Advances in Neural Information Processing Systems 19 (pp. 1281-1288). pdf ps    

Bibtex citation

@inproceedings{SheHuiKan07,
   author = "Christian R. Shelton and Wesley Huie and Kin Fai Kan",
   title = "Chained Boosting",
   booktitle = "Advances in Neural Information Processing Systems 19",
   booktitleabbr = "{NIPS}-2006",
   year = 2007,
   pages = "1281--1288",
}

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