Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Driven Randomized Learning of Feedforward Neural Networks   Randomized methods of neural network learning suffer from a problem with the
generation of random parameters as they are difficult to set optimally to
obtain a good projection space. The standard method draws the parameters from a
fixed interval which is independent of the data scope and activation function
type. This does not lead to good results in the approximation of the strongly
nonlinear functions. In this work, a method which adjusts the random
parameters, representing the slopes and positions of the sigmoids, to the
target function features is proposed. The method randomly selects the input
space regions, places the sigmoids in these regions and then adjusts the
sigmoid slopes to the local fluctuations of the target function. This brings
very good results in the approximation of the complex target functions when
compared to the standard fixed interval method and other methods recently
proposed in the literature.

17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an