Bootstrap - Inspired Techniques in Computation Intelligence

by: R Polikar

Signal Processing Magazine, IEEE, Vol. 24, No. 4. (2007), pp. 59-72.

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摘要

This article is about the success story of a seemingly simple yet extremely powerful approach that has recently reached a celebrity status in statistical and engineering sciences. The hero of this story - bootstrap resampling - is relatively young, but the story itself is a familiar one within the scientific community: a mathematician or a statistician conceives and formulates a theory that is first developed by fellow mathematicians and then brought to fame by other professionals, typically engineers, who point to many applications that can benefit from just such an approach. Signal processing boasts some of the finest examples of such stories, such as the classic story of Fourier transforms or the more contemporary tale of wavelet transforms.

@article{polikar07bootstrap-inspired, abstract = {This article is about the success story of a seemingly simple yet extremely powerful approach that has recently reached a celebrity status in statistical and engineering sciences. The hero of this story - bootstrap resampling - is relatively young, but the story itself is a familiar one within the scientific community: a mathematician or a statistician conceives and formulates a theory that is first developed by fellow mathematicians and then brought to fame by other professionals, typically engineers, who point to many applications that can benefit from just such an approach. Signal processing boasts some of the finest examples of such stories, such as the classic story of Fourier transforms or the more contemporary tale of wavelet transforms.}, author = {Polikar, R. }, booktitle = {Signal Processing Magazine, IEEE}, citeulike-article-id = {3017856}, doi = {10.1109/MSP.2007.4286565}, journal = {Signal Processing Magazine, IEEE}, keywords = {adaboost, bagging, boosting, data-fusion, incremental-learning, missing-feature, mixture-of-experts}, number = {4}, pages = {59--72}, posted-at = {2008-07-18 13:51:20}, priority = {5}, title = {Bootstrap - Inspired Techniques in Computation Intelligence}, url = {http://dx.doi.org/10.1109/MSP.2007.4286565}, volume = {24}, year = {2007} }

RIS record

TY - JOUR ID - polikar07bootstrap-inspired L3 - citeulike-article-id:3017856 TI - Bootstrap - Inspired Techniques in Computation Intelligence T2 - Signal Processing Magazine, IEEE JF - Signal Processing Magazine, IEEE VL - 24 IS - 4 SP - 59 EP - 72 N2 - This article is about the success story of a seemingly simple yet extremely powerful approach that has recently reached a celebrity status in statistical and engineering sciences. The hero of this story - bootstrap resampling - is relatively young, but the story itself is a familiar one within the scientific community: a mathematician or a statistician conceives and formulates a theory that is first developed by fellow mathematicians and then brought to fame by other professionals, typically engineers, who point to many applications that can benefit from just such an approach. Signal processing boasts some of the finest examples of such stories, such as the classic story of Fourier transforms or the more contemporary tale of wavelet transforms. KW - adaboost KW - bagging KW - boosting KW - data-fusion KW - incremental-learning KW - missing-feature KW - mixture-of-experts AU - Polikar, R PY - 2007/// UR - http://dx.doi.org/10.1109/MSP.2007.4286565 ER -

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