The guaranteed estimation of the Lipschitz classifier accuracy: Confidence set approach

@inproceedings{Timofeev2012TheGE,
  title={The guaranteed estimation of the Lipschitz classifier accuracy: Confidence set approach},
  author={A. V. Timofeev},
  year={2012}
}
Abstract This paper introduces an original method for the guaranteed estimation of the Lipschitz classifier accuracy in the case of a large number of classes. The solution was obtained as a finite closed set of alternative hypotheses, which contains an object of classification with probability of not less than the specified value. Thus, the classification is represented by a set of hypothetical classes. In this case, the smaller the cardinality of the discrete set of hypothetical classes is… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 16 REFERENCES

Distance-Based Classification with Lipschitz Functions

  • J. Mach. Learn. Res.
  • 2003
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Speaker identificationwithwhispered speech based onmodified LFCCparameters and featuremapping

J. H. L. Hansen
  • IEEE international conference on acoustics , speech and signal processing
  • 2009
VIEW 1 EXCERPT

SPRO: a free speech signal processing toolkit (version 4.0.1)

G. Gravier
  • Learning Research,
  • 2003
VIEW 1 EXCERPT

Support vector machines

M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, B. Scholkopf
  • IEEE Intelligent Systems
  • 1998