Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning with Smooth Hinge Losses   Due to the non-smoothness of the Hinge loss in SVM, it is difficult to obtain
a faster convergence rate with modern optimization algorithms. In this paper,
we introduce two smooth Hinge losses $\psi_G(\alpha;\sigma)$ and
$\psi_M(\alpha;\sigma)$ which are infinitely differentiable and converge to the
Hinge loss uniformly in $\alpha$ as $\sigma$ tends to $0$. By replacing the
Hinge loss with these two smooth Hinge losses, we obtain two smooth support
vector machines(SSVMs), respectively. Solving the SSVMs with the Trust Region
Newton method (TRON) leads to two quadratically convergent algorithms.
Experiments in text classification tasks show that the proposed SSVMs are
effective in real-world applications. We also introduce a general smooth convex
loss function to unify several commonly-used convex loss functions in machine
learning. The general framework provides smooth approximation functions to
non-smooth convex loss functions, which can be used to obtain smooth models
that can be solved with faster convergent optimization algorithms.

20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math