Training Data Modeling Using Counter Propagation Networks for Improved Generalization Abilities
Hirokazu Madokoro, Koya Sato, Masaki Ishii · 2006
This paper presents a new method for improved generalization abilities of Back Propagation Networks (BPNs). The method is based on topological data mapping used in Counter Propagation Networks (CPNs). The CPNs save input data into a category map while retaining topological data structures. We used weights and labels of the category map for new training data of the BPN. Our method provides the following benefits: 1) the number of training data can be controlled by changing category map sizes; 2) interpolation training data can be produced under the topological space; and 3) overlapping training data can be avoided through the use of Winner-Take-All competition. Experimental results show that expanded training data improved the generalization ability.