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      • University of Waikato Research
      • Computing and Mathematical Sciences
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      Random convolution ensembles

      Mayo, Michael
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      Random convolution ensembles.pdf
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      DOI
       10.1007/978-3-540-77255-2_24
      Link
       www.springerlink.com
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      Citation
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      Mayo M. (2007). Random convolution ensembles. In Advances in Multimedia Information Processing – PCM 2007, 8th Pacific Rim Conference on Multimedia, Hong Kong, China, December 11-14, 2007, Proceedings (pp. 216-225). Berlin: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/1379
      Abstract
      A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images in the labeled training set. The base classifiers are then learned using features extracted from these randomly transformed versions of the training data, and the result is a highly diverse ensemble of image classifiers. This approach is evaluated on a benchmark pedestrian detection dataset and shown to be effective.
      Date
      2007
      Type
      Conference Contribution
      Publisher
      Springer
      Collections
      • Computing and Mathematical Sciences Papers [1441]
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