Abstract
We consider the classification problem by learning from samples drawn from a non-identical sequence of probability measures. The learning algorithm is from Tikhonov regularization schemes associated with convex loss functions and reproducing kernel Hilbert spaces. Our main goal is to provide satisfactory estimates for the excess misclassification error of the produced classifiers.
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Communicated by Ding-Xuan Zhou.
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Xiao, QW., Pan, ZW. Learning from non-identical sampling for classification. Adv Comput Math 33, 97–112 (2010). https://doi.org/10.1007/s10444-009-9123-x
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DOI: https://doi.org/10.1007/s10444-009-9123-x
Keywords
- Reproducing kernel Hilbert space
- Binary classification
- Excess misclassification error
- Non-identical sampling