.png){kind=icon}
返回
Bootstrapping complex functions
.png){kind=icon}
PRE
AI
DOI：10.1016/j.nahs.2006.12.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We formulate a new family of bootstrap algorithms suitable for learning non-Boolean functions from data. Within the Algorithmic Inference framework, the key idea is to consider a population of functions that are compatible with the observed sample. We generate items of this population from standard random seeds and reverse seed probabilities on the items. In this way we may compute in principle, and effectively achieve on paradigmatic examples, direct estimates and confidence intervals for any kind of complex function underlying the observed data according to any hypothesis on the randomness affecting the sample. (c) 2007 Elsevier Ltd. All rights reserved.
Keyword:
Algorithmic inference
New bootstrap methods
Learning non-Boolean functions
Nonlinear regression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
N
IF:
4.1
论文数:
1.4K
被引数:
3.1K
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏
分享收藏
查看更多内容