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    Wyświetlanie 1-3 z 3
    Tytuł:
    A rainfall forecasting method using machine learning models and its application to the Fukuoka city case
    Autorzy:
    Sumi, S. M.
    Zaman, M. F.
    Hirose, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/331290.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    maszyna ucząca się
    metoda wielomodelowa
    przetwarzanie wstępne
    rainfall forecasting
    machine learning
    multi model method
    preprocessing
    model ranking
    Opis:
    In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 841-854
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem
    Autorzy:
    Czarnowski, I.
    Jędrzejowicz, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/907819.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    redukcja danych
    komputerowe uczenie się
    optymalizacja
    system wieloagentowy
    data reduction
    machine learning
    A-Team
    optimization
    multi-agent system
    Opis:
    The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning process executed by a team of agents (A-Team). Several A-Team architectures with agents executing the simulated annealing and tabu search procedures are proposed and investigated. The paper includes a detailed description of the proposed approach and discusses the results of a validating experiment.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2011, 21, 1; 57-68
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation
    Autorzy:
    Kantavat, P.
    Kijsirikul, B.
    Songsiri, P.
    Fukui, K. I.
    Numao, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/330532.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    support vector machine
    multi-class classification
    generalization error
    decision tree
    maszyna wektorów wsparcia
    klasyfikacja wieloklasowa
    błąd generalizacji
    drzewo decyzyjne
    Opis:
    We propose new methods for support vector machines using a tree architecture for multi-class classification. In each node of the tree, we select an appropriate binary classifier, using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classifier, and train a new classifier for use in the classification phase. The proposed methods can work in time complexity between O(log2 N) and O(N), where N is the number of classes. We compare the performance of our methods with traditional techniques on the UCI machine learning repository using 10-fold cross-validation. The experimental results show that the methods are very useful for problems that need fast classification time or those with a large number of classes, since the proposed methods run much faster than the traditional techniques but still provide comparable accuracy.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 705-717
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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