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A Fast NonParametric Density Estimation Algorithm
"... INTRODUCTION The use of probability density estimation in data analysis is well established [15, 12, 11, 2]. In the nonparametric case no assumption is made about the type of the distribution from which the samples are drawn. This is in contrast to parametric estimation in which the density is ass ..."
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INTRODUCTION The use of probability density estimation in data analysis is well established [15, 12, 11, 2]. In the nonparametric case no assumption is made about the type of the distribution from which the samples are drawn. This is in contrast to parametric estimation in which the density
Colour Image Segmentation by NonParametric Density Estimation
 in Colour Space, in Proc. BMVC 2001, BMVA
, 2001
"... A novel colour image segmentation routine, based on clustering pixels in colour space using nonparametric density estimation, is described. Although the basic methodology is well known, several important improvements to the previous work in this area are introduced. The density is estimated at a se ..."
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Cited by 4 (1 self)
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A novel colour image segmentation routine, based on clustering pixels in colour space using nonparametric density estimation, is described. Although the basic methodology is well known, several important improvements to the previous work in this area are introduced. The density is estimated at a
Efficient Online NonParametric Density Estimation
"... Nonparametric density estimation has broad applications in computational finance especially in cases where high frequency data are available. However, the technique is often intractable, given the run times necessary to evaluate a density. We present a new and efficient algorithm based on multip ..."
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Nonparametric density estimation has broad applications in computational finance especially in cases where high frequency data are available. However, the technique is often intractable, given the run times necessary to evaluate a density. We present a new and efficient algorithm based
Colour Image Segmentation by NonParametric Density Estimation in Colour Space
 in Colour Space, in Proc. BMVC 2001, BMVA
, 2001
"... A novel colour image segmentation routine, based on clustering pixels in colour space using nonparametric density estimation, is described. Although the basic methodology is well known, several important improvements to the previous work in this area are introduced. The density is estimated at a ..."
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A novel colour image segmentation routine, based on clustering pixels in colour space using nonparametric density estimation, is described. Although the basic methodology is well known, several important improvements to the previous work in this area are introduced. The density is estimated
Practical Nonparametric Density Estimation on a Transformation Group for Vision
 PROC. IEEE CVPR 2003
, 2003
"... It is now common practice in machine vision to define the variability in an object's appearance in a factored manner, as a combination of shape and texture transformations. In this context, we present a simple and practical method for estimating nonparametric probability densities over a group ..."
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Cited by 12 (0 self)
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It is now common practice in machine vision to define the variability in an object's appearance in a factored manner, as a combination of shape and texture transformations. In this context, we present a simple and practical method for estimating nonparametric probability densities over a
Temporal Feedback for Tweet Search with NonParametric Density Estimation
"... This paper investigates the temporal cluster hypothesis: in search tasks where time plays an important role, do relevant documents tend to cluster together in time? We explore this question in the context of tweet search and temporal feedback: starting with an initial set of results from a baseline ..."
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retrieval model, we estimate the temporal density of relevant documents, which is then used for result reranking. Our contributions lie in a method to characterize this temporal density function using kernel density estimation, with and without human relevance judgments, and an approach to integrating
GAKDEBayes: An Evolutionary Wrapper Method Based on NonParametric Density Estimation Applied to Bioinformatics Problems
"... Abstract. This paper presents an evolutionary wrapper method for feature selection that uses a nonparametric density estimation method and a Bayesian Classifier. Nonparametric methods are a good alternative for scarce and sparse data, as in Bioinformatics problems, since they do not make any assum ..."
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Abstract. This paper presents an evolutionary wrapper method for feature selection that uses a nonparametric density estimation method and a Bayesian Classifier. Nonparametric methods are a good alternative for scarce and sparse data, as in Bioinformatics problems, since they do not make any
RATE OF CONVERGENCE FOR NON PARAMETRIC DENSITY ESTIMATION IN LINEAR PROCESS By
"... Rate of convergence to normality for the density estimators of Kernel type is obtained when the observations are from a stationary linear processes. At first, the case of estimating the density at a fixed point is considered and latter on, it is extended for estimating joint density. Also the proble ..."
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Rate of convergence to normality for the density estimators of Kernel type is obtained when the observations are from a stationary linear processes. At first, the case of estimating the density at a fixed point is considered and latter on, it is extended for estimating joint density. Also
Modelling Shapes with Uncertainties: Higher Order Polynomials, Variable Bandwidth Kernels and non Parametric Density Estimation
"... In this paper, we introduce a new technique for shape modelling in the space of implicit polynomials. Registration consists of recovering an optimal onetoone transformation of a higher order polynomial along with uncertainties measures that are determined according to the covariance matrix of the ..."
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Cited by 3 (1 self)
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of the correspondences at the zero isosurface. In the modelling phase, these measures are used to weight the importance of the training samples phase according to a variable bandwidth nonparametric density estimation process. The selection of the most appropriate kernels to represent the training set is done through
Shape Learning Framework: Spline based registration and nonparametric density estimator in the space of higher order polynomials
, 2005
"... In this report, we introduce a new technique to shape modelling in the space of implicit polynomials. Registration consists of recovering an optimal onetoone transformation of a higher order polynomial along with uncertainties measures that are determined according to the covariance matrix of the ..."
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of the correspondences at the zero isosurface. Such measures are used to weight the importance of the training samples in the modelling phase according to a variable bandwidth nonparametric density estimation process. The selection of the most appropriate kernels to represent the training set is done through the maximum
Results 1  10
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