期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 98, 期 463, 页码 724-734出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/016214503000000639
关键词
classification; generalization error; margins; machine learning; metric entropy; support vector machine
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.
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