Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Oleg Okun"'
Autor:
Helen Priisalu, Oleg Okun
Publikováno v:
Artificial Intelligence in Medicine. 45:151-162
Objective: We explore the link between dataset complexity, determining how difficult a dataset is for classification, and classification performance defined by low-variance and low-biased bolstered resubstitution error made by k-nearest neighbor clas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5f03988eb624b844f4568fd0af78a74
https://doi.org/10.1016/j.artmed.2008.08.004
https://doi.org/10.1016/j.artmed.2008.08.004
Publikováno v:
Pattern Recognition and Image Analysis. 17:612-620
High-dimensional data visualization is a more complex process than the ordinary dimensionality reduction to two or three dimensions. Therefore, we propose and evaluate a novel four-step visualization approach that is built upon the combination of thr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eed897b0686bd5d6d159900e235dcb7e
https://doi.org/10.1134/s1054661807040220
https://doi.org/10.1134/s1054661807040220
Autor:
Oleg Okun
Publikováno v:
Pattern Recognition and Image Analysis. 17:621-630
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6728dd377fb69cfdc7d08d23eedf396d
https://doi.org/10.1134/s1054661807040232
https://doi.org/10.1134/s1054661807040232
Autor:
Oleg Okun, Helen Priisalu
Publikováno v:
Signal Processing. 87:2260-2267
We propose a data reduction method based on fuzzy clustering and nonnegative matrix factorisation. In contrast to different variants of data set editing typically used for data reduction, our method is completely unsupervised, i.e., it does not need
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0920106e9a4e676a73671ae69d13b8ef
https://doi.org/10.1016/j.sigpro.2007.02.006
https://doi.org/10.1016/j.sigpro.2007.02.006
Autor:
Oleg Okun
Publikováno v:
Pattern Recognition and Image Analysis. 16:19-22
Two proteins may be structurally similar but not have significant sequence similarity. Protein fold recognition is an approach usually applied in this case. It does not rely on sequence similarity and can be achieved with relevant features extracted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe78a44e0eaf4594dc21446b7dc01f38
https://doi.org/10.1134/s1054661806010068
https://doi.org/10.1134/s1054661806010068
Publikováno v:
Pattern Recognition. 38:1764-1767
The locally linear embedding (LLE) algorithm belongs to a group of manifold learning methods that not only merely reduce data dimensionality, but also attempt to discover a true low dimensional structure of the data. In this paper, we propose an incr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05c5b12f1116581876931581f4d0a3d3
https://doi.org/10.1016/j.patcog.2005.04.006
https://doi.org/10.1016/j.patcog.2005.04.006
Publikováno v:
International Journal on Document Analysis and Recognition. 2:132-144
The existing skew estimation techniques usually assume that the input image is of high resolution and that the detectable angle range is limited. We present a more generic solution for this task that overcomes these restrictions. Our method is based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd1afe5f80b1b0982a6d031247a108e1
https://doi.org/10.1007/s100320050043
https://doi.org/10.1007/s100320050043
This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Pract
