Results 241 to 250 of about 411,514 (294)

Re-Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic Perception. [PDF]

Adv Sci (Weinh)
Wang J, Li S.
europepmc   +1 more source

Responding to climate change: assessing the current situation and influencing factors of forest carbon sinks in China. [PDF]

Sci Rep
Shi L, Xu M, Tan Y, Wu Y, Zhao J.
europepmc   +1 more source

A Fast Nonlinear Sparse Model for Blind Image Deblurring. [PDF]

J Imaging
Zhang Z, Guo Z, Xu Z, Chen H, Wang C, Song Y, Lai J, Ji Y, Li Z.   +8 more
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Solubility of Glibenclamide in supercritical solvent versus pressure and temperature via development of machine learning and rain optimization algorithm. [PDF]

Sci Rep
Alotaibi HF, Hsu CY, Sead FF, Yadav A, Jyothi SR, Mishra S, Purohit B, Ashirova A, Eshchanov T, Chauhan AS.   +9 more
europepmc   +1 more source

Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset.

JMIR Med Inform
Fernández-Narro D, Ferri P, Gutiérrez-Sacristán A, García-Gómez JM, Sáez C.   +4 more
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Robust kernels for kernel density estimation

Economics Letters, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Shaoping, Li, Ang, Wen, Kuangyu, Wu, Ximing   +3 more
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Generalized Kernel Density Estimator

Theory of Probability & Its Applications, 2000
Summary: We introduce a new class of nonparametric density estimators. It includes the classical kernel density estimators as well as the popular Abramson's estimator. We show that the generalized estimators may perform much better than the classical one if the distribution has a heavy tail.
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Online Discriminative Kernel Density Estimator With Gaussian Kernels

IEEE Transactions on Cybernetics, 2014
We propose a new method for a supervised online estimation of probabilistic discriminative models for classification tasks. The method estimates the class distributions from a stream of data in the form of Gaussian mixture models (GMMs). The reconstructive updates of the distributions are based on the recently proposed online kernel density estimator ...
Matej, Kristan, Ales, Leonardis
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VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES

Australian Journal of Statistics, 1990
SummaryThe term “variable kernel density estimate” is sometimes used to mean a kernel density estimate employing a different bandwidth for each data point, and sometimes to denote a kernel density estimate with bandwidth a function of estimation location.
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Bootstrapping kernel spectral density estimates with kernel bandwidth estimation

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004
We address the problem of confidence interval estimation of spectral densities using the bootstrap. Of special interest is the choice of the kernel global bandwidth. First, we investigate resampling based techniques for the choice of the bandwidth.
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