article · 2008

Sample Complexity of Classification

John Langford, Ibm Research

Cited by 0Abstract and details only

Abstract

We discuss basic sample complexity theory and it&apos;s impact on classification success evaluation, implications for learning algorithm design, and uses in learning algorithm execution. There are a two important implications of the results presented here: (1) Common practices for reporting results in classification should change to us the test set bound. (2) Train set bounds can sometimes be used to directly motivate learning algorithms.

Cite this paper

APA 7

Langford, J., & Research, I. (2008). Sample Complexity of Classification.

MLA 9

Langford, John, and Ibm Research. “Sample Complexity of Classification.” 2008.

Chicago

Langford, John, and Ibm Research. 2008. “Sample Complexity of Classification.”

Harvard

Langford, J. and Research, I. (2008) ‘Sample Complexity of Classification’.

BibTeX

@article{langford2008sample,
  title = {{Sample Complexity of Classification}},
  author = {Langford, John and Research, Ibm},
  year = {2008}
}

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