  In this paper, we introduce a new machine learning theory based on
multi-channel parallel adaptation for rule discovery. This theory is
distinguished from the familiar parallel-distributed adaptation theory of
neural networks in terms of channel-based convergence to the target rules. We
show how to realize this theory in a learning system named CFRule. CFRule is a
parallel weight-based model, but it departs from traditional neural computing
in that its internal knowledge is comprehensible. Furthermore, when the model
converges upon training, each channel converges to a target rule. The model
adaptation rule is derived by multi-level parallel weight optimization based on
gradient descent. Since, however, gradient descent only guarantees local
optimization, a multi-channel regression-based optimization strategy is
developed to effectively deal with this problem. Formally, we prove that the
CFRule model can explicitly and precisely encode any given rule set. Also, we
prove a property related to asynchronous parallel convergence, which is a
critical element of the multi-channel parallel adaptation theory for rule
learning. Thanks to the quantizability nature of the CFRule model, rules can be
extracted completely and soundly via a threshold-based mechanism. Finally, the
practical application of the theory is demonstrated in DNA promoter recognition
and hepatitis prognosis prediction.
