Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Consistency of support vector machines for forecasting the evolution of
  an unknown ergodic dynamical system from observations with unknown noise   We consider the problem of forecasting the next (observable) state of an
unknown ergodic dynamical system from a noisy observation of the present state.
Our main result shows, for example, that support vector machines (SVMs) using
Gaussian RBF kernels can learn the best forecaster from a sequence of noisy
observations if (a) the unknown observational noise process is bounded and has
a summable $\alpha$-mixing rate and (b) the unknown ergodic dynamical system is
defined by a Lipschitz continuous function on some compact subset of
$\mathbb{R}^d$ and has a summable decay of correlations for Lipschitz
continuous functions. In order to prove this result we first establish a
general consistency result for SVMs and all stochastic processes that satisfy a
mixing notion that is substantially weaker than $\alpha$-mixing.

19
The categories of this paper are: eess
1
The categories of this paper are: cs
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an