ABSTRACT - A Bayesian Neural Network

[Anders Lansner]

The following paper will appear in the first (March -89) issue of the
International Journal for Neural Systems (World Scientific Publishing):


A One-Layer Feedback Artificial Neural Network
with a Bayesian Learning Rule

by

Anders Lansner and \rjan Ekeberg
Dept. of Numerical Analysis and Computing Science
Royal Institute of Technology, Stockholm, Sweden


A probabilistic artificial neural network is presented. It is of a
one-layer, feedback-coupled type with graded units. The learning rule
is derived from Bayes rule. Learning is regarded as collecting
statistics and recall as a statistical inference process. Units
corresponds to events and connections come out as compatibility
coefficients in a logarithmic combination rule. The input to a unit
via connections from other active units affects the a posteriori
belief in the event in question.

The new model is compared to an earlier binary model with respect to
storage capacity, noise tolerance etc. in a content addressable memory
(CAM) task. The new model is a real time network and some results on
the reaction time for associative recall are given. The scaling of
learning and relaxation operations is considered together with issues
related to representation of information in one-layered artificial
neural networks. An extension with complex units is discussed.


Preprint requests to:

Anders  Lansner
NADA
KTH
S-100 44 Stockholm
SWEDEN

<email: ala at bion.kth.se>

Various earlier versions of this model are also described in:

Lansner A. and Ekeberg \. (1985): Reliability and Speed of Recall in an
	Associative Network. IEEE Trans. Pattern Analysis and Machine
	Intelligence 7(4), 490-498.

Lansner A. and Ekeberg \ (1987): AN Associative Network Solving the 4-bit
	ADDER Problem. Proc. ICNN, II-549, San Diego, June 21-24, 1987.

Ekeberg \. and Lansner A. (1988): Automatic Generation of Internal
	Representation in a Probabilisitic Artificial Neural Network. Proc.
	nEuro'88, Neural Networks from Models to Applications, Personnaz L.
	and Dreyfus G. (eds.), I.D.S.E.T., Paris, 1989, 178-186.