Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "pattern" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-5 z 5
    Tytuł:
    Automatic detection and counting of platelets in microscopic image
    Autorzy:
    Burduk, R.
    Krawczyk, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/333065.pdf
    Data publikacji:
    2010
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie wzorców
    nauczanie maszynowe
    analiza obrazu
    pattern recognition
    bioinformatic
    machine learning
    image analysis
    platelet
    Opis:
    In this paper we present a machine learning-based approach for detecting platelet cells in microscopic smear images. Counting how many platelets appeared in each smear image is one of the basic tasks done in many laboratories. In many cases this is still done by a human — laboratory technician. Due to very small size and often great quantity of those cells, precise estimating of the number of platelets is not a trivial task. As in all man-dependent problems the whole process is very sensitive to errors, time-consuming and its accuracy is limited by human perception. We propose alternative, fully automatic solution that is free of those drawbacks. Our idea is based on the combination of techniques driven from two fields of modern computer science: the image analysis and pattern recognition ⁄ machine learning. It not only reduces the error rate, but, what is more important, also decreases the time needed for each smear image analysis. The obtained results are very satisfying and our solution is more precise than estimation based on human perception. This will improve the quality of laboratory work and allow to save time that can be spent on other important tasks.
    Źródło:
    Journal of Medical Informatics & Technologies; 2010, 16; 173-178
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Handling class label noise in medical pattern classification systems
    Autorzy:
    Sáez, J. A.
    Krawczyk, B.
    Woźniak, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333813.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    machine learning
    pattern classification
    class noise
    noise filtering
    decision support systems
    uczenie maszynowe
    klasyfikacja wzorców
    filtracja zakłóceń
    filtracja szumów
    systemy wspomagania decyzji
    Opis:
    Pattern classification systems play an important role in medical decision support. They allow to automatize and speed-up the data analysis process, while being able to handle complex and massive amounts of information and discover new knowledge. However, their quality is based on the classification models built, which require a training set. In supervised classification we must supply class labels to each training sample, which is usually done by domain experts or some automatic systems. As both of these approaches cannot be deemed as flawless, there is a chance that the dataset is corrupted by class noise. In such a situation, class labels are wrongly assigned to objects, which may negatively affect the classifier training process and impair the classification performance. In this contribution, we analyze the usefulness of existing tools to deal with class noise, known as noise filtering methods, in the context of medical pattern classification. The experiments carried out on several real-world medical datasets prove the importance of noise filtering as a pre-processing step and its beneficial influence on the obtained classification accuracy.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 123-130
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Combined classifier based on feature space partitioning
    Autorzy:
    Woźniak, M.
    Krawczyk, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/331294.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rozpoznawanie wzorców
    system klasyfikujący wielokrotny
    algorytm grupowania
    algorytm selekcji
    algorytm ewolucyjny
    pattern recognition
    combined classifier
    multiple classifier system
    clustering algorithm
    selection algorithm
    evolutionary algorithm
    Opis:
    This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier, which makes a decision based on a weighted combination of the discriminant functions of the individual classifiers selected for the committee. The weights mentioned above are dependent not only on the classifier identifier, but also on the class number. The proposed approach is based on the results of previous works, where it was proven that such a combined classifier method could achieve significantly better results than simple voting systems. The proposed modification was evaluated through computer experiments, carried out on diverse benchmark datasets. The results are very promising in that they show that, for most of the datasets, the proposed method outperforms similar techniques based on the clustering and selection approach.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 855-866
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hypertension diagnosis using compound pattern recognition methods
    Autorzy:
    Krawczyk, B.
    Woźniak, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333544.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie wzorców
    metody złożone
    zespoły klasyfikatora
    uczenie maszynowe
    niesymetryczna klasyfikacja
    uznanie dwustopniowe
    pattern recognition
    compound methods
    classifier ensembles
    machine learning
    unbalanced classification
    two-stage recognition
    Opis:
    The paper presents a hypertension type classification task where the decisions should be made only on the basis of blood pressure, general information and basis biochemical data. This problem has a great importance to the medical decision support systems, yet results achieved so far are not satisfactory. When the canonical approaches tend to fail we should look for the compound pattern recognition systems, such as multiple classifiers systems. This article presents the results of an experimental investigation of the pool of compound classifiers which have their origin in classifiers ensembles, random forest, and random subspace. Presented methods returned good, satisfactory results, outperforming canonical approaches for this problem.
    Źródło:
    Journal of Medical Informatics & Technologies; 2011, 18; 41-50
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Classification techniques for non-invasive recognition of liver fibrosis stage
    Autorzy:
    Krawczyk, B.
    Woźniak, M.
    Orczyk, T.
    Porwik, P.
    Musialik, J.
    Błońska-Fajfrowska, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/332969.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    uczenie maszynowe
    systemy wielo-klasyfikatorów
    informatyka medyczna
    zwłóknienie wątroby
    wirusowe zapalenie wątroby typu C
    machine learning
    multiple classifier systems
    compound pattern recognition
    medical informatics
    liver fibrosis
    hepatitis C
    Opis:
    Contemporary medicine should provide high quality diagnostic services while at the same time remaining as comfortable as possible for a patient. Therefore novel non-invasive disease recognition methods are becoming one of the key issues in the health services domain. Analysis of data from such examinations opens an interdisciplinary bridge between the medical research and artificial intelligence. The paper presents application of machine learning techniques to biomedical data coming from indirect examination method of the liver fibrosis stage. Presented approach is based on a common set of non-invasive blood test results. The performance of four different compound machine learning algorithms, namely Bagging, Boosting, Random Forest and Random Subspaces, is examined and grid search method is used to find the best setting of their parameters. Extensive experimental investigations, carried out on a dataset collected by authors, show that automatic methods achieve a satisfactory level of the fibrosis level recognition and may be used as a real-time medical decision support system for this task.
    Źródło:
    Journal of Medical Informatics & Technologies; 2012, 20; 121-127
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies