Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Jacek Koronacki"'
Publikováno v:
Information Sciences. 330:74-87
Dimensionality reduction that preserves certain characteristics of data is needed for numerous reasons. In this work we focus on data coming from a mixture of Gaussian distributions and we propose a method that preserves the distinctness of the clust
Publikováno v:
Applied Mathematics and Computation. 256:591-601
There are numerous measures designed to evaluate quality of a given data grouping in terms of its distinctness and between-cluster separation. However, there seems to be no efficient method to assess distinctness of the intrinsic structure within dat
Autor:
Jan Komorowski, Jakub Mieczkowski, Marcin Kruczyk, Nicholas Baltzer, Michał Dramiński, Jacek Koronacki
Publikováno v:
Fundamenta Informaticae. 127:273-288
An important step prior to constructing a classifier for a very large data set is feature selection. With many problems it is possible to find a subset of attributes that have the same discriminative power as the full data set. There are many feature
Autor:
Jan Komorowski, Alvaro Rada-Iglesias, Stefan Enroth, Claes Wadelius, Michał Dramiński, Jacek Koronacki
Publikováno v:
Bioinformatics (Oxford, England). 24(1)
Motivation: Pre-selection of informative features for supervised classification is a crucial, albeit delicate, task. It is desirable that feature selection provides the features that contribute most to the classification task per se and which should
Publikováno v:
Advances in Soft Computing ISBN: 3540250565
Intelligent Information Systems
Intelligent Information Systems
In the paper, three conceptually simple but computer-intensive versions of an approach to selecting informative genes for classification are proposed. All of them rely on multiple construction of a tree classifier for many training sets randomly chos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c67a7c0f8938e34d2e5ae1eba6897cd1
https://doi.org/10.1007/3-540-32392-9_36
https://doi.org/10.1007/3-540-32392-9_36