Topic
Resampling
About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.
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01 Dec 2005
TL;DR: An experimental bias-variance analysis of bagged and random aggregated ensembles of SVMs is presented in order to verify their theoretical variance reduction properties and suggests new directions for research to improve on classical bagging.
Abstract: Recently, bias-variance decomposition of error has been used as a tool to study the behavior of learning algorithms and to develop new ensemble methods well suited to the bias-variance characteristics of base learners. We propose methods and procedures, based on Domingo's unified bias-variance theory, to evaluate and quantitatively measure the bias-variance decomposition of error in ensembles of learning machines. We apply these methods to study and compare the bias-variance characteristics of single support vector machines (SVMs) and ensembles of SVMs based on resampling techniques, and their relationships with the cardinality of the training samples. In particular, we present an experimental bias-variance analysis of bagged and random aggregated ensembles of SVMs in order to verify their theoretical variance reduction properties. The experimental bias-variance analysis quantitatively characterizes the relationships between bagging and random aggregating, and explains the reasons why ensembles built on small subsamples of the data work with large databases. Our analysis also suggests new directions for research to improve on classical bagging.
70 citations
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TL;DR: The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.
Abstract: Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems. Many of these systems possess strong memory, with which future information can help sharpen the inference about the current state. By providing theoretical justification of several existing algorithms and introducing several new ones, we study systematically how to construct efficient SMC algorithms to take advantage of the “future” information without creating a substantially high computational burden. The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.
70 citations
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TL;DR: In this article, the authors study an autoregressive time series model with a possible change in the regression parameters and obtain approximate estimates to the critical values for change-point tests through various bootstrapping methods.
70 citations
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70 citations
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TL;DR: In this paper, the influence of sampling interval on the accuracy of estimates for selected trail impact problems was examined using a resampling simulation method using a complete census of four impact-types on 70 backcountry trails in the Great Smoky Mountains National Park was utilized as the base dataset for the analyses.
69 citations