Institute of Information Theories and Applications FOI ITHEA
One of the problems in AI tasks solving by neurocomputing methods is a considerable training time.
This problem especially appears when it is needed to reach high quality in forecast reliability or pattern
recognition. Some formalised ways for increasing of networks’ training speed without loosing of precision are
proposed here. The offered approaches are based on the Sufficiency Principle, which is formal representation
of the aim of a concrete task and conditions (limitations) of their solving . This is development of the
concept that includes the formal aims’ description to the context of such AI tasks as classification, pattern
recognition, estimation etc.