We lifted 2 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Zhenyu-LIAO/RMT4LSSVM | canonical | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| get_kernel | Ran | Zhenyu-LIAO/RMT4LSSVM/RMT4LSSVM.py code served (permissive licence) · get_code("c7c79b1cfda5e989") |
| get_stat | Not yet run | Zhenyu-LIAO/RMT4LSSVM/RMT4LSSVM.py code served (permissive licence) · get_code("5f2fd641f6b2ad26") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this article, a large dimensional performance analysis of kernel least squares support vector machines (LS-SVMs) is provided under the assumption of a two-class Gaussian mixture model for the input data. Building upon recent advances in random matrix theory, we show, when the dimension of data $p$ and their number $n$ are both large, that the LS-SVM decision function can be well approximated by a normally distributed random variable, the mean and variance of which depend explicitly on a local behavior of the kernel function. This theoretical result is then applied to the MNIST and Fashion-MNIST datasets which, despite their non-Gaussianity, exhibit a convincingly close behavior. Most importantly, our analysis provides a deeper understanding of the mechanism into play in SVM-type methods and in particular of the impact on the choice of the kernel function as well as some of their theoretical limits in separating high dimensional Gaussian vectors.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1701.02967")
get_code_for_paper("1701.02967")
have("1701.02967")
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