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- W1672534408 abstract "This paper provides new characterization of data complexity for margin-based methods also known as SVMs, kernel methods etc. Under the predictive learning setting, the complexity of a given data set is directly related to model complexity, i.e. the flexibility of a set of admissible models used to describe this data. There are two distinct approaches to model complexity control: traditional model-based where complexity is controlled via parameterization of admissible models, and margin-based where complexity is controlled by the size of margin (in a specially designed empirical loss function). This paper emphasizes the role of margin for complexity control, and proposes a simple index for data complexity suitable for classification and regression problems." @default.
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- W1672534408 date "2006-01-05" @default.
- W1672534408 modified "2023-09-24" @default.
- W1672534408 title "Characterization of data complexity for SVM methods" @default.
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- W1672534408 doi "https://doi.org/10.1109/ijcnn.2005.1555975" @default.
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