Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Helen Priisalu"'
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
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:
O. Okun, Helen Priisalu
Publikováno v:
IJCNN
In this paper, ensembles of k-nearest neighbors classifiers are explored for gene expression cancer classification, where each classifier is linked to a randomly selected subset of genes. It is experimentally demonstrated using five datasets that suc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::313855f32f17369e96d3b339556d3c20
https://doi.org/10.1109/ijcnn.2008.4634077
https://doi.org/10.1109/ijcnn.2008.4634077
Autor:
Oleg Okun, Helen Priisalu
Publikováno v:
Supervised and Unsupervised Ensemble Methods and their Applications ISBN: 9783540789802
Gene expression levels are useful in discriminating between cancer and normal examples and/or between different types of cancer. In this chapter, ensembles of k-nearest neighbors are employed for gene expression based cancer classification. The ensem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c53a59023978b960b6fc98201bf9161
https://doi.org/10.1007/978-3-540-78981-9_6
https://doi.org/10.1007/978-3-540-78981-9_6
Autor:
Helen Priisalu, Oleg Okun
Publikováno v:
Applications of Fuzzy Sets Theory ISBN: 9783540733997
WILF
WILF
When applied to supervised classification problems, dataset complexity determines how difficult a given dataset to classify. Since complexity is a nontrivial issue, it is typically defined by a number of measures. In this paper, we explore complexity
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9bc07c067db49b457c39a8041013135
https://doi.org/10.1007/978-3-540-73400-0_61
https://doi.org/10.1007/978-3-540-73400-0_61
Autor:
Helen Priisalu, Oleg Okun
Publikováno v:
Pattern Recognition and Image Analysis ISBN: 9783540728481
IbPRIA (2)
IbPRIA (2)
Random forest is a collection (ensemble) of decision trees. It is a popular ensemble technique in pattern recognition. In this article, we apply random forest for cancer classification based on gene expression and address two issues that have been so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bcbc6ec06474eb8fdd2253d975c1f80d
https://doi.org/10.1007/978-3-540-72849-8_61
https://doi.org/10.1007/978-3-540-72849-8_61
Autor:
Oleg Okun, Helen Priisalu
Publikováno v:
EURASIP Journal on Advances in Signal Processing. 2006
Linear and unsupervised dimensionality reduction via matrix factorization with nonnegativity constraints is studied. Because of these constraints, it stands apart from other linear dimensionality reduction methods. Here we explore nonnegative matrix
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b435163fa43bec5157a036764d96b44
https://doi.org/10.1155/asp/2006/71817
https://doi.org/10.1155/asp/2006/71817
Publikováno v:
Machine Learning: ECML 2005 ISBN: 9783540292432
ECML
ECML
In this paper, dimensionality reduction via matrix factorization with nonnegativity constraints is studied. Because of these constraints, it stands apart from other linear dimensionality reduction methods. Here we explore nonnegative matrix factoriza
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6fe567ddd992a7b14ac84520be5885f9
https://doi.org/10.1007/11564096_67
https://doi.org/10.1007/11564096_67