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This paper presents a comparative experimental study of the multidimensional indexing methods based on the approximation approach. We are particularly interested in the LSH family, which provides efficient index structures and solves the dimensionality curse problem. The goal is to understand the performance gain and the behavior of this family of methods on large-scale databases. E2LSH is compared...
In this paper we examine a class of multiple-input, single-output (MISO) nonlinear systems of the block-oriented structure. In particular, we focus on MISO Hammerstein systems being the cascade connection of a multivariate nonlinearity with a linear dynamical subsystem. In order to alleviate an apparent curse of dimensionality occurring in the problem of estimating the nonlinearity, we propose to...
In this paper we develop a semi-parametric approach to the problem of identification of multivariate Hammerstein systems. A nonlinearity in general multivariate Hammerstein systems is represented by projecting the d-dimensional input signal onto one dimensional subset which, in turn, is mapped by a univariate nonparametric function to an internal signal of the system. Such a parsimonious representation...
The DDCM is a dynamic program (DP) with discrete controls, which is very important in understanding agents' behavior in various settings. This paper begins with the essential features and hypothesis. On the one hand, to estimate the model with two sources of heterogeneity when the number of periods is small, it obtains non-linear bias corrected estimator (NBC) and minimized integrated mean square...
Particle filtering is a widely used Monte Carlo method to approximate the posterior density in non-linear filtering. Unlike the Kalman filter, the particle filter deals with non-linearity, multi-modality or non Gaussianity. However, recently, it has been observed that particle filtering can be inefficient when the dimension of the system is high. We discuss the effect of dimensionality on the Monte...
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