Support Vector Machines for Classification in Nonstandard Situations

@article{Lin2004SupportVM,
  title={Support Vector Machines for Classification in Nonstandard Situations},
  author={Yi Lin and Yoonkyung Lee and Grace Wahba},
  journal={Machine Learning},
  year={2004},
  volume={46},
  pages={191-202}
}
The majority of classification algorithms are developed for the standard situation in which it is assumed that the examples in the training set come from the same distribution as that of the target population, and that the cost of misclassification into different classes are the same. However, these assumptions are often violated in real world settings. For some classification methods, this can often be taken care of simply with a change of threshold; for others, additional effort is required… 
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