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    Wyświetlanie 1-4 z 4
    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
    Tytuł:
    Can learning vector quantization be an alternative to SVM and deep learning? : recent trends and advanced variants of learning vector quantization for classification learning
    Autorzy:
    Villmann, T.
    Bohnsack, A.
    Kaden, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/91630.pdf
    Data publikacji:
    2017
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    classification learning
    vector quantization
    prototype based learning
    Opis:
    Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classification of vector data, intuitively introduced by Kohonen. The prototype adaptation scheme relies on its attraction and repulsion during the learning providing an easy geometric interpretability of the learning as well as of the classification decision scheme. Although deep learning architectures and support vector classifiers frequently achieve comparable or even better results, LVQ models are smart alternatives with low complexity and computational costs making them attractive for many industrial applications like intelligent sensor systems or advanced driver assistance systems. Nowadays, the mathematical theory developed for LVQ delivers sufficient justification of the algorithm making it an appealing alternative to other approaches like support vector machines and deep learning techniques. This review article reports current developments and extensions of LVQ starting from the generalized LVQ (GLVQ), which is known as the most powerful cost function based realization of the original LVQ. The cost function minimized in GLVQ is an soft-approximation of the standard classification error allowing gradient descent learning techniques. The GLVQ variants considered in this contribution, cover many aspects like bordersensitive learning, application of non-Euclidean metrics like kernel distances or divergences, relevance learning as well as optimization of advanced statistical classification quality measures beyond the accuracy including sensitivity and specificity or area under the ROC-curve. According to these topics, the paper highlights the basic motivation for these variants and extensions together with the mathematical prerequisites and treatments for integration into the standard GLVQ scheme and compares them to other machine learning approaches. For detailed description and mathematical theory behind all, the reader is referred to the respective original articles. Thus, the intention of the paper is to provide a comprehensive overview of the stateof- the-art serving as a starting point to search for an appropriate LVQ variant in case of a given specific classification problem as well as a reference to recently developed variants and improvements of the basic GLVQ scheme.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 1; 65-81
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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