Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inferring linear and nonlinear Interaction networks using neighborhood
  support vector machines   In this paper, we consider modelling interaction between a set of variables
in the context of time series and high dimension. We suggest two approaches.
The first is similar to the neighborhood lasso when the lasso model is replaced
by a support vector machine (SVMs). The second is a restricted Bayesian network
adapted for time series. We show the efficiency of our approaches by
simulations using linear, nonlinear data set and a mixture of both.

22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess