Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A neural model for multi-expert architectures   We present a generalization of conventional artificial neural networks that
allows for a functional equivalence to multi-expert systems. The new model
provides an architectural freedom going beyond existing multi-expert models and
an integrative formalism to compare and combine various techniques of learning.
(We consider gradient, EM, reinforcement, and unsupervised learning.) Its
uniform representation aims at a simple genetic encoding and evolutionary
structure optimization of multi-expert systems. This paper contains a detailed
description of the model and learning rules, empirically validates its
functionality, and discusses future perspectives.

11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
21
The categories of this paper are: adap-org