统计学习和VC理论

Tutorial Guide. ISCAS 2001. IEEE International Symposium on Circuits and Systems (Cat. No.01TH8573) Pub Date : 2001-05-06 DOI:10.1109/TUTCAS.2001.946954

P. Bartlett

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引用次数: 4

摘要

本文将统计学习理论应用于监督学习问题。模式识别涵盖，包括Vapnik-Chervonenkis (VC)理论和支持向量机(svm)，神经网络和决策树的含义。对尺度敏感的维度给出了真实的预测。文章最后通过对大利润分类的分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Statistical learning and VC theory

The article applies statistical learning theory to the supervised learning problem. Pattern recognition is covered, including Vapnik-Chervonenkis (VC) theory and the implications for support vector machines (SVMs), neural networks and decision trees. Real predictions are given for scale-sensitive dimensions. The article concludes by analysing large margin classification.

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来源期刊

Tutorial Guide. ISCAS 2001. IEEE International Symposium on Circuits and Systems (Cat. No.01TH8573)

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