نتایج جستجو برای: nearest neighbor sampling method
تعداد نتایج: 1803146 فیلتر نتایج به سال:
Because of the massive increase in the size of the data it becomes troublesome to perform effective analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of dat...
This paper gives a concise overview of the techniques we have used to find out the degree of measuring the quality of rendered images and a level of noise in particular. First part of the paper presents designed and conducted psychophysical experiment involving human subjective judgment. Then, two of the existing numerical image comparison methods are considered in the context of assessing the ...
K-nearest neighbor (k-NN) classification is a powerful and simple method for classification. k-NN classifiers approximate a Bayesian classifier for a large number of data samples. The accuracy of k-NN classifier relies on the distance metric used for calculating nearest neighbor and features used for instances in training and testing data. In this paper we use deep neural networks (DNNs) as a f...
We consider the problem of using nearest neighbor methods to provide a conditional probability estimate, P (y|a), when the number of labels y is large and the labels share some underlying structure. We propose a method for learning label embeddings (similar to error-correcting output codes (ECOCs)) to model the similarity between labels within a nearest neighbor framework. The learned ECOCs and...
In our searches, we use a lower plane-wave basis cutoff of 500 eV and a coarser k-point sampling density of 2π×0.07 Å−1 along with ultrasoft pseudopotentials generated by the castep code. Once low-enthalpy candidate structures have been identified, we proceed with the higher basis cutoff energy of 800 eV and k-point sampling density of 2π×0.03 Å−1. All results presented in the main paper use th...
Point sampling is an important intermediate step for a variety of computer graphics applications, and specialized sampling strategies have been developed to satisfy the requirements of each problem. In this article, we present a technique to generate a stratified sampling of 3D models that is applicable across many domains. The algorithm voxelizes the model and selects one sample per voxel, res...
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