Pattern classification by geometrical learning of binary neural networks
This paper considers the use of binary neural networks for pattern classification. An expand-and-truncate learning (ETL) algorithm is used to determine the required number of neurons as well as the connecting weights in a three-layered feedforward network for classifying input patterns. The ETL algorithm is guaranteed to find a network for any binary-to-binary mappings. The ETL algorithm's performance in pattern classification is tested using a breast cancer database that have been used for benchmarking performance other machine learning methods.