.png){kind=icon}
.png){kind=icon}
Return
Bootstrapping complex functions
.png){kind=icon}
PRE
AI
DOI：10.1016/j.nahs.2006.12.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
Algorithmic inference
New bootstrap methods
Learning non-Boolean functions
Nonlinear regression
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
N
IF:
4.1
Papers:
1.4K
Citations:
3.1K