In particle filtering, resampling is the only step that cannot be fully parallelized. Recently, we have proposed algorithms for distributed resampling implemented on architecture...
Balakumar Balasingam, Miodrag Bolic, Petar M. Djur...
This paper addresses the problem of the optimal design of numerical experiments for the construction of nonlinear surrogate models. We describe a new method, called learner disagre...
Abstract. Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the nu...
Moving-Least-Squares (MLS) Surfaces undergoing large deformations need periodic regeneration of the point set (point-set resampling) so as to keep the point-set density quasi-unif...
Background: Many analyses of microarray association studies involve permutation, bootstrap resampling and crossvalidation, that are ideally formulated as embarrassingly parallel c...
Ivo D. Shterev, Sin-Ho Jung, Stephen L. George, Ko...
The transforming and rendering of discrete objects, such as traditional images (with or without depths) and volumes, can be considered as resampling problem – objects are recons...
— One way to handle data mining problems where class prior probabilities and/or misclassification costs between classes are highly unequal is to resample the data until a new, d...
Particle filtering (PF) for dynamic Bayesian networks (DBNs) with discrete-state spaces includes a resampling step which concentrates samples according to their relative weight in ...
Abstract. Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the small sample size (SSS) prob...