Zobrazeno 1 - 10
of 4 547
pro vyhledávání: '"Variable kernel density estimation"'
Autor:
Klebanov, Ilja
Variable kernel density estimation allows the approximation of a probability density by the mean of differently stretched and rotated kernels centered at given sampling points $y_n\in\mathbb{R}^d,\ n=1,\dots,N$. Up to now, the choice of the correspon
Externí odkaz:
http://arxiv.org/abs/1805.01729
Autor:
Terrell, George R., Scott, David W.
Publikováno v:
The Annals of Statistics, 1992 Sep 01. 20(3), 1236-1265.
Externí odkaz:
https://www.jstor.org/stable/2242011
Autor:
Hazelton, Martin L.1
Publikováno v:
Australian & New Zealand Journal of Statistics. Sep2003, Vol. 45 Issue 3, p271-284. 14p.
Autor:
Zhen Zhang, Yanning Zhang
Publikováno v:
Neurocomputing. 134:30-37
Robust estimation with high break down point is an important and fundamental topic in computer vision, machine learning and many other areas. Traditional robust estimator with a break down point more than 50%, for illustration, Random Sampling Consen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::70f8de0ed878e40f2908c7e7581f6128
https://doi.org/10.1016/j.neucom.2012.12.076
https://doi.org/10.1016/j.neucom.2012.12.076
Autor:
Martin L. Hazelton
Publikováno v:
Australian New Zealand Journal of Statistics. 45:271-284
Summary This paper considers the problem of selecting optimal bandwidths for variable (sample-point adaptive) kernel density estimation. A data-driven variable bandwidth selector is proposed, based on the idea of approximating the log-bandwidth funct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::853b6d3a06af6d37ddff4b4727ab74d6
https://doi.org/10.1111/1467-842x.00283
https://doi.org/10.1111/1467-842x.00283
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 31
Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dime
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3c210adaede222715018bb71b1b7892
https://doi.org/10.1609/aaai.v31i1.10885
https://doi.org/10.1609/aaai.v31i1.10885
Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dime
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1399::afd40ee5481bd93cc6669b0fec29c0d8
https://hdl.handle.net/10394/26441
https://hdl.handle.net/10394/26441
Publikováno v:
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
In recent years, kernel density estimation has been exploited by computer scientists to model several important problems in machine learning, bioinformatics, and computer vision. However, in case the dimension of the data set is high, then the conven
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a55762c47764721efcf2120fd2ae861
https://doi.org/10.1109/ijcnn.2005.1556374
https://doi.org/10.1109/ijcnn.2005.1556374