On ψ-learning

• 成果类型: Article
• 署名作者: Shen, XT; Tseng, GC; Zhang, XG; Wong, WH
• 署名单位: University System of Ohio; Ohio State University; University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Tsinghua University; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214503000000639
• 发表日期: 2003
• 页码: 724-734
• 关键词: convergence; estimators; rates

摘要

The concept of large margins have been recognized as an important principle in analyzing learning methodologies, including boosting, neural networks, and support vector machines (SVMs). However, this concept alone is not adequate for learning in nonseparable cases. We propose a learning methodology, called psi-learning, that is derived from a direct consideration of generalization errors. We provide a theory for psi-learning and show that it essentially attains the optimal rates of convergence in two learning examples. Finally, results from simulation studies and from breast cancer classification confirm the ability of psi-learning to outperform SVM in generalization.