A Novel Net that Learns Sequential Decision Process

Abstract

We propose a new scheme to construct neural networks to classify pat-terns. The new scheme has several novel features: 1. We focus attention on the important attributes of patterns in ranking order. Extract the most important ones first and the less important ones later. 2. In training we use the information as a measure instead of the error function. 3. A multi-percept ron-like architecture is formed auomatically. Decision is made according to the tree structure of learned attributes. This new scheme is expected to self-organize and perform well in large scale problems.