Theory of Interacting Neural Networks
(April 2002)Abstract
In this contribution we give an overview over recent work on the theory of
interacting neural networks. The model is defined in Section 2. The typical
teacher/student scenario is considered in Section 3. A static teacher network
is presenting training examples for an adaptive student network. In the case of
multilayer networks, the student shows a transition from a symmetric state to
specialisation. Neural networks can also generate a time series. Training on
time series and predicting it are studied in Section 4. When a network is
trained on its own output, it is interacting with itself. Such a scenario has
implications on the theory of prediction algorithms, as discussed in Section 5.
When a system of networks is trained on its minority decisions, it may be
considered as a model for competition in closed markets, see Section 6. In
Section 7 we consider two mutually interacting networks. A novel phenomenon is
observed: synchronisation by mutual learning. In Section 8 it is shown, how
this phenomenon can be applied to cryptography: Generation of a secret key over
a public channel.
Description
citeulike