Wszystkie pola: discriminant coordinates - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A learning algorithm combining functional discriminant coordinates and functional principal components
Autorzy:: Górecki, Tomasz
Krzyśko, Mirosław
Powiązania:: https://bibliotekanauki.pl/articles/729818.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:: functional principal components
functional discriminant coordinates
Opis:: A new type of discriminant space for functional data is presented, combining the advantages of a functional discriminant coordinate space and a functional principal component space. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 35 functional data sets (time series). Experiments show that constructed combined space provides a higher quality of classification of LDA method compared with component spaces.
Źródło:: Discussiones Mathematicae Probability and Statistics; 2014, 34, 1-2; 127-141
1509-9423
Pojawia się w:: Discussiones Mathematicae Probability and Statistics
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Effect of choice of dissimilarity measure on classification efficiency with nearest neighbor method
Autorzy:: Górecki, Tomasz
Powiązania:: https://bibliotekanauki.pl/articles/729668.pdf
Data publikacji:: 2005
Wydawca:: Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:: nearest neighbor method
discriminant coordinates
dissimilarity measures
estimators of classification error
Opis:: In this paper we will precisely analyze the nearest neighbor method for different dissimilarity measures, classical and weighed, for which methods of distinguishing were worked out. We will propose looking for weights in the space of discriminant coordinates. Experimental results based on a number of real data sets are presented and analyzed to illustrate the benefits of the proposed methods. As classical dissimilarity measures we will use the Euclidean metric, Manhattan and post office metric. We gave the first two metrics weights and now these measures are not metrics because the triangle inequality does not hold. Howeover, it does not make them useless for the nearest neighbor classification method. Additionally, we will analyze different methods of tie-breaking.
Źródło:: Discussiones Mathematicae Probability and Statistics; 2005, 25, 2; 217-239
1509-9423
Pojawia się w:: Discussiones Mathematicae Probability and Statistics
Dostawca treści:: Biblioteka Nauki

Artykuł

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