Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Similarity networks for classification: a case study in the Horse Colic
  problem   This paper develops a two-layer neural network in which the neuron model
computes a user-defined similarity function between inputs and weights. The
neuron transfer function is formed by composition of an adapted logistic
function with the mean of the partial input-weight similarities. The resulting
neuron model is capable of dealing directly with variables of potentially
different nature (continuous, fuzzy, ordinal, categorical). There is also
provision for missing values. The network is trained using a two-stage
procedure very similar to that used to train a radial basis function (RBF)
neural network. The network is compared to two types of RBF networks in a
non-trivial dataset: the Horse Colic problem, taken as a case study and
analyzed in detail.

22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg