Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Statistical Mechanics of Support Vector Networks   Using methods of Statistical Physics, we investigate the generalization
performance of support vector machines (SVMs), which have been recently
introduced as a general alternative to neural networks. For nonlinear
classification rules, the generalization error saturates on a plateau, when the
number of examples is too small to properly estimate the coefficients of the
nonlinear part. When trained on simple rules, we find that SVMs overfit only
weakly. The performance of SVMs is strongly enhanced, when the distribution of
the inputs has a gap in feature space.

31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys