Resampling

About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.

A cluster-based resampling method for pseudo-relevance feedback

Abstract: Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select better pseudo-relevant documents based on the relevance model. The main idea is to use document clusters to find dominant documents for the initial retrieval set, and to repeatedly feed the documents to emphasize the core topics of a query. Experimental results on large-scale web TREC collections show significant improvements over the relevance model. For justification of the resampling approach, we examine relevance density of feedback documents. A higher relevance density will result in greater retrieval accuracy, ultimately approaching true relevance feedback. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback. This result indicates that the proposed method is effective for pseudo-relevance feedback.

A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM

Abstract: A new statistical method for regional climate simulations is introduced. Its simulations are constrained only by the parameters of a linear regression line for a characteristic climatological variable. Simulated series are generated by resampling from segments of observation series such that the resulting series comply with the prescribed regression parameters and possess realistic annual cycles and persistence. The resampling guarantees that the simulated series are physically consistent both with respect to the combinations of different meteorological variables and to their spatial distribution at each time step. The resampling approach is evaluated by means of a cross validation experiment for the Elbe river basin: Its simulations are compared both to an observed climatology and to data simulated by a dynamical RCM. This cross validation shows that the approach is able to reproduce the observed climatology with respect to statistics such as long-term means, persistence features (e.g., dry spells) and extreme events. The agreement of its simulations with the observational data is much closer than for the RCM data.

Particle-systems implementation of the PHD multitarget tracking filter

Abstract: We report here on the implementation of a particle systems approximation to the probability hypothesis density (PHD). The PHD of the multitarget posterior density has the property that, given any volume of state space, the integral of the PHD over that volume yields the expected number of targets present in the volume. As in the single target setting, upon receipt of an observation, the particle weights are updated, taking into account the sensor likelihood function, and then propagated forward in time by sampling from a Markov transition density. We also incorporate resampling and regularization into our implementation, introducing the new concept of cluster resampling.

Implicit sampling for particle filters

Abstract: We present a particle-based nonlinear filtering scheme, related to recent work on chainless Monte Carlo, designed to focus particle paths sharply so that fewer particles are required. The main features of the scheme are a representation of each new probability density function by means of a set of functions of Gaussian variables (a distinct function for each particle and step) and a resampling based on normalization factors and Jacobians. The construction is demonstrated on a standard, ill-conditioned test problem.