Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extreme Learning Machine for land cover classification   This paper explores the potential of extreme learning machine based
supervised classification algorithm for land cover classification. In
comparison to a backpropagation neural network, which requires setting of
several user-defined parameters and may produce local minima, extreme learning
machine require setting of one parameter and produce a unique solution. ETM+
multispectral data set (England) was used to judge the suitability of extreme
learning machine for remote sensing classifications. A back propagation neural
network was used to compare its performance in term of classification accuracy
and computational cost. Results suggest that the extreme learning machine
perform equally well to back propagation neural network in term of
classification accuracy with this data set. The computational cost using
extreme learning machine is very small in comparison to back propagation neural
network.

3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn