NEURAL NETWORKS AND RELATED METHODS FOR CLASSIFICATION

• 成果类型: Article
• 署名作者: RIPLEY, BD
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• 发表日期: 1994
• 页码: 409-437
• 关键词: projection pursuit regression; stochastic-approximation; Decision Trees; CONVERGENCE; algorithm; optimization; models; DESIGN; rates; nets

摘要

Feed-forward neural networks are now widely used in classification problems, whereas non-linear methods of discrimination developed in the statistical field are much less widely known. A general framework for classification is set up within which methods from statistics, neural networks, pattern recognition and machine learning can be compared. Neural networks emerge as one of a class of flexible non-linear regression methods which can be used to classify via regression. Many interesting issues remain, including parameter estimation, the assessment of the classifiers and in algorithm development.