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On ψ-learning
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DOI：10.1198/016214503000000639.png)
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摘要
En 中文
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.
Keyword:
classification
generalization error
margins
machine learning
metric entropy
support vector machine
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