Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Large Dimensional Analysis of Least Squares Support Vector Machines   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.

35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
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The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph