| 基于α-混合序列的学习机器一致收敛速率的界 |
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| 引用本文： | 邹斌,李落清,万成高. 基于α-混合序列的学习机器一致收敛速率的界[J]. 应用概率统计, 2007, 23(2) |
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| 作者姓名： | 邹斌 李落清 万成高 |
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| 作者单位： | 湖北大学数学与计算机科学学院,武汉,430062 |
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| 摘 要： | Vapnik,Cucker和Smale已经证明了,当样本的数目趋于无限时,基于独立同分布序列学习机器的经验风险会一致收敛到它的期望风险.本文把这些基于独立同分布序列的结果推广到了α-混合序列,应用Markov不等式得到了基于α-混合序列的学习机器一致收敛速率的界.
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| 关 键 词： | 学习机器 一致收敛 混合序列 |
The Bound for the Rate of Uniform Convergence for Learning Machine Based on α-mixing Sequence |
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| ZOU BIN,LI LUOQING,WAN CHENGGAO. The Bound for the Rate of Uniform Convergence for Learning Machine Based on α-mixing Sequence[J]. Chinese Journal of Applied Probability and Statisties, 2007, 23(2) |
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| Authors: | ZOU BIN LI LUOQING WAN CHENGGAO |
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| Abstract: | It has been shown previously by Vapnik, Cucker and Smale that, the empirical risks based on an independent and identically distributed (i.i.d.) sequence must uniformly converge to their expected risks for learning machines as the number of samples approaches infinity. This paper extends the results to the case where the i.i.d, sequence replaced by α-mixing sequence. It establishes the rate of uniform convergence for learning machine based on α-mixing sequence by applying Markov's inequality. |
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| Keywords: | Learning machine uniform convergence mixing sequence |
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