Sparse kernel approximations for efficient classification and detection
Efficient learning with non-linear kernels is often based on extracting features from the data that linearise the kernel. While most constructions aim at obtaining low-dimensional and dense features, in this work we explore high-dimensional and sparse ones. We give a method to compute sparse feature...
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| Médium: | Conference item |
| Jazyk: | English |
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IEEE
2012
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