title: Evolving logic networks with real-valued inputs for fast incremental learning.
abstract: In this paper, we present a neural network structure and a fast incremental learning algorithm using this network. The proposed network structure, named Evolving Logic Networks for Real-valued inputs (ELN-R), is a data structure for storing and using the knowledge. A distinctive feature of ELN-R is that the previously learned knowledge stored in ELN-R can be used as a kind of building block in constructing new knowledge. Using this feature, the proposed learning algorithm can enhance the stability and plasticity at the same time, and as a result, the fast incremental learning can be realized. The performance of the proposed scheme is shown by a theoretical analysis and an experimental study on two benchmark problems.
authors.affiliation: School of Electrical Engineering and Computer Science, Automation and Systems Research Institute, Seoul National University, Seoul 151-744, Korea.
authors.forename: Myoung Soo
authors.initials: MS
authors.lastname: Park
authors.forename: Jin Young
authors.initials: JY
authors.lastname: Choi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer