统计学习理论
计算机科学
算法学习理论
统计理论
人工智能
一般化
机器学习
计算学习理论
学习理论
功能(生物学)
无监督学习
大概是正确的学习
信息论
理论计算机科学
支持向量机
数学
统计
数学分析
数学教育
生物
进化生物学
出处
期刊:IEEE Transactions on Neural Networks
[Institute of Electrical and Electronics Engineers]
日期:1999-01-01
卷期号:10 (5): 988-999
被引量:5723
摘要
Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new types of learning algorithms (called support vector machines) based on the developed theory were proposed. This made statistical learning theory not only a tool for the theoretical analysis but also a tool for creating practical algorithms for estimating multidimensional functions. This article presents a very general overview of statistical learning theory including both theoretical and algorithmic aspects of the theory. The goal of this overview is to demonstrate how the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems. A more detailed overview of the theory (without proofs) can be found in Vapnik (1995). In Vapnik (1998) one can find detailed description of the theory (including proofs).
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