Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stability of the Stochastic Gradient Method for an Approximated Large
  Scale Kernel Machine   In this paper we measured the stability of stochastic gradient method (SGM)
for learning an approximated Fourier primal support vector machine. The
stability of an algorithm is considered by measuring the generalization error
in terms of the absolute difference between the test and the training error.
Our problem is to learn an approximated kernel function using random Fourier
features for a binary classification problem via online convex optimization
settings. For a convex, Lipschitz continuous and smooth loss function, given
reasonable number of iterations stochastic gradient method is stable. We showed
that with a high probability SGM generalizes well for an approximated kernel
under given assumptions.We empirically verified the theoretical findings for
different parameters using several data sets.

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The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math