Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Source localization in an ocean waveguide using supervised machine
  learning   Source localization in ocean acoustics is posed as a machine learning problem
in which data-driven methods learn source ranges directly from observed
acoustic data. The pressure received by a vertical linear array is preprocessed
by constructing a normalized sample covariance matrix (SCM) and used as the
input. Three machine learning methods (feed-forward neural networks (FNN),
support vector machines (SVM) and random forests (RF)) are investigated in this
paper, with focus on the FNN. The range estimation problem is solved both as a
classification problem and as a regression problem by these three machine
learning algorithms. The results of range estimation for the Noise09 experiment
are compared for FNN, SVM, RF and conventional matched-field processing and
demonstrate the potential of machine learning for underwater source
localization..

2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga