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Comparison theorems on large-margin learning
This paper studies binary classification problem associated with a family of loss functions called large-margin unified machines (LUM), which offers a natural bridge between distribution-based likelihood approaches and margin-based approaches.
Total stability of kernel methods
However, the actually used kernel often depends on one or on a few hyperparameters or the kernel is even data dependent in a much more complicated manner.
A short note on extension theorems and their connection to universal consistency in machine learning
Statistical machine learning plays an important role in modern statistics and computer science.
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