Affiliations: Department of Computer Science, Artificial Intelligence Research Group, Iowa State University, 226 Atanasoff Hall, Ames, IA 50011, USA
Correspondence:
[*]
Corresponding author. Current address: HRL Laboratories, 3011 Malibu Canyon Road, Malibu, CA 90265-4799, USA. E-mail: yang@wins.hrl.com
Note: [†] Current address: All State Research and Planning Center, Menlo Park, CA 94040, USA. E-mail: rpare@allstate.com
Note: [1] Partially supported by the National Science Foundation (through grant IRI-9409580) and the John Deere Foundation.
Abstract: Multi-layer networks of threshold logic units (TLU) offer an attractive framework for the design of pattern classification systems. A new constructive neural network learning algorithm (DistAl) based on inter-pattern distance is introduced. DistAl constructs a single hidden layer of hyperspherical threshold neurons. Each neuron is designed to determine a cluster of training patterns belonging to the same class. The weights and thresholds of the hidden neurons are determined directly by comparing the inter-pattern distances of the training patterns. This offers a significant advantage over other constructive learning algorithms that use an iterative (and often time consuming) weight modification strategy to train individual neurons. The individual clusters (represented by the hidden neurons) are combined by a single output layer of threshold neurons. The speed of DistAl makes it a good candidate for datamining and knowledge acquisition from large datasets. The paper presents results of experiments using several artificial and real-world datasets. The results demonstrate that DistAl compares favorably with other learning algorithms for pattern classification.
