Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Random Parameters in Feedforward Neural Networks with Random
  Hidden Nodes: Drawbacks of the Standard Method and How to Improve It   The standard method of generating random weights and biases in feedforward
neural networks with random hidden nodes, selects them both from the uniform
distribution over the same fixed interval. In this work, we show the drawbacks
of this approach and propose a new method of generating random parameters. This
method ensures the most nonlinear fragments of sigmoids, which are most useful
in modeling target function nonlinearity, are kept in the input hypercube. In
addition, we show how to generate activation functions with uniformly
distributed slope angles.

13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph