Resampling (a.k.a. bootstrapping) is a computationallyintensive statistical technique for estimating the sampling distribution of an estimator. Resampling is used in many machine learning algorithms, including ensemble methods, active learning, and feature selection. Resampling techniques generate pseudosamples from an underlying population by sampling with replacement from a single sample data...

In an extension of the work of Liu and Singh (1992), we consider resampling estimates for the variance of the least squares estimator in linear regression models. Second order terms in asymptotic expansions of these estimates are derived. By comparing the second order terms, certain generalised bootstrap schemes are seen to be theoretically better than other resampling techniques under very gen...

In this paper we present a novel resampling model for extractive meeting summarization. With resampling based on the output of a baseline classifier, our method outperforms previous research in the field. Further, we compare an existing resampling technique with our model. We report on an extensive series of experiments on a large meeting corpus which leads to classification improvement in weig...

This paper proposed a fast resampling method for digital image. Recently, using mobile device is very popular. It is very important to make the images suitable for every different mobile device. There are many methods to change the size of image. The most important methods are image resampling and image retargeting. This paper proposed a fast resampling method. The method is suitable for mobile...

Abstract The duration of extremes in time leads to a phenomenon known as clustering high values, with strong impact on risk assessment. extremal index is measure developed within Extreme Value Theory that quantifies the degree values. In this work we will consider cycles estimator introduced Ferreira and (Ann Inst Henri Poincare Probab Stat 54(2):587–605, 2018). A reduced bias based Jackknife m...