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Wyświetlanie 1-3 z 3
Tytuł:
Improving interpretability: combined use of LVQ and ARTMAP in decision support
Autorzy:
Kwok, H. F.
Giorgi, A.
Raffone, A.
Powiązania:
https://bibliotekanauki.pl/articles/309473.pdf
Data publikacji:
2005
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
learning vector quantization
ARTMAP
decision support systems
ischemic heart disease
Opis:
The learning vector quantization (LVQ) network was used to classify the ECG ST segment into different morphological categories. Due to the lack of data in the ST elevation categories, the classifier was only trained to identify different types of ST depressions (horizontal, upsloping and downsloping). The accuracies were 91%, 85% and 65% respectively for the training, validation and testing data respectively. Despite the low accuracy for the testing data, most of the mis-classifications were downsloping ST depression being classified as horizontal ST depression. We concluded that more data and more training are needed in order to train the LVQ to recognize other morphological types of ST deviation and to improve the accuracy.
Źródło:
Journal of Telecommunications and Information Technology; 2005, 4; 129-132
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
One-Dimensional Kohonens Lvq Nets for Multidimensional Patterns Recognition
Autorzy:
Skubalska-Rafajłowicz, E.
Powiązania:
https://bibliotekanauki.pl/articles/911149.pdf
Data publikacji:
2000
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
krzywa przestrzenna
rozpoznawanie obrazów
space-filling curve
pattern recognition
learning vector quantization
reduction of dimension
Opis:
A new neural network based pattern recognition algorithm is proposed. The method consists in preprocessing the multidimensional data, using a space-filling curve based transformation into the unit interval, and employing Kohonen's vector quantization algorithms (of SOM and LVQ types) in one dimension. The space-filling based transformation preserves the theoretical Bayes risk. Experiments show that such an approach can produce good or even better error rates than the classical LVQ performed in a multidimensional space.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 767-778
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Nursing logistics activities in massive services
Autorzy:
Simić, D.
Powiązania:
https://bibliotekanauki.pl/articles/333536.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
systemy klasyfikacji
samoorganizujące się mapy
nursing logistics activities
classification system
learning vector quantization
self-organizing maps
Opis:
Hybrid patient classification system in nursing logistics activities is discussed in this paper. Hybrid classification model is based on two of the most used competitive artificial neural network algorithms that use learning vector quantization models (LVQ) and self-organizing maps (SOM). In general, the history of patient classification in nursing dates back to the period of Florence Nightingale. The first and the foremost condition for providing quality nursing care, which is measured by care standards, and determined by number of hours of actual care, is the appropriate number of nurses. It is possible to discus three types of experimental results. First result type could be assessment for risk of falling measured by Mors scale and pressure sores risk measured by Braden scale. Both of them are assessed by LVQ. Hybrid LVQ-SOM model is used for second result type, which presents the time for nursing logistics activities. The third type is possibility to predict appropriate number of nurses for providing quality nursing care. This research was conducted on patients from Institute of Neurology, Clinical Centre of Vojvodina.
Źródło:
Journal of Medical Informatics & Technologies; 2011, 18; 77-84
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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