Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A robust machine learning method for cell-load approximation in wireless
  networks   We propose a learning algorithm for cell-load approximation in wireless
networks. The proposed algorithm is robust in the sense that it is designed to
cope with the uncertainty arising from a small number of training samples. This
scenario is highly relevant in wireless networks where training has to be
performed on short time scales because of a fast time-varying communication
environment. The first part of this work studies the set of feasible rates and
shows that this set is compact. We then prove that the mapping relating a
feasible rate vector to the unique fixed point of the non-linear cell-load
mapping is monotone and uniformly continuous. Utilizing these properties, we
apply an approximation framework that achieves the best worst-case performance.
Furthermore, the approximation preserves the monotonicity and continuity
properties. Simulations show that the proposed method exhibits better
robustness and accuracy for small training sets in comparison with standard
approximation techniques for multivariate data.

0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph