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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2015
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1506.01520 |
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| _version_ | 1866917005378977792 |
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| author | van Rooyen, Brendan Menon, Aditya Krishna Williamson, Robert C. |
| author_facet | van Rooyen, Brendan Menon, Aditya Krishna Williamson, Robert C. |
| contents | Many leading classification algorithms output a classifier that is a weighted average of kernel evaluations. Optimizing these weights is a nontrivial problem that still attracts much research effort. Furthermore, explaining these methods to the uninitiated is a difficult task. Letting all the weights be equal leads to a conceptually simpler classification rule, one that requires little effort to motivate or explain, the mean. Here we explore the consistency, robustness and sparsification of this simple classification rule. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1506_01520 |
| institution | arXiv |
| publishDate | 2015 |
| record_format | arxiv |
| spellingShingle | Sparse Robust Classification via the Kernel Mean van Rooyen, Brendan Menon, Aditya Krishna Williamson, Robert C. Machine Learning Many leading classification algorithms output a classifier that is a weighted average of kernel evaluations. Optimizing these weights is a nontrivial problem that still attracts much research effort. Furthermore, explaining these methods to the uninitiated is a difficult task. Letting all the weights be equal leads to a conceptually simpler classification rule, one that requires little effort to motivate or explain, the mean. Here we explore the consistency, robustness and sparsification of this simple classification rule. |
| title | Sparse Robust Classification via the Kernel Mean |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1506.01520 |