Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Binary output layer of feedforward neural networks for solving
  multi-class classification problems   Considered in this short note is the design of output layer nodes of
feedforward neural networks for solving multi-class classification problems
with r (bigger than or equal to 3) classes of samples. The common and
conventional setting of output layer, called "one-to-one approach" in this
paper, is as follows: The output layer contains r output nodes corresponding to
the r classes. And for an input sample of the i-th class, the ideal output is 1
for the i-th output node, and 0 for all the other output nodes. We propose in
this paper a new "binary approach": Suppose r is (2^(q minus 1), 2^q] with q
bigger than or equal to 2, then we let the output layer contain q output nodes,
and let the ideal outputs for the r classes be designed in a binary manner.
Numerical experiments carried out in this paper show that our binary approach
does equally good job as, but uses less output nodes than, the traditional
one-to-one approach.

27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph