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    Wyświetlanie 1-3 z 3
    Tytuł:
    Survival analysis on data streams: Analyzing temporal events in dynamically changing environments
    Autorzy:
    Shaker, A.
    Hüllermeier, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/331440.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    data stream
    survival analysis
    event history analysis
    earthquake data
    Twitter data
    strumień danych
    analiza przeżycia
    Opis:
    In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history analysis) is an established statistical method for the study of temporal “events” or, more specifically, questions regarding the temporal distribution of the occurrence of events and their dependence on covariates of the data sources. To make this method applicable in the setting of data streams, we propose an adaptive variant of a model that is closely related to the well-known Cox proportional hazard model. Adopting a sliding window approach, our method continuously updates its parameters based on the event data in the current time window. As a proof of concept, we present two case studies in which our method is used for different types of spatio-temporal data analysis, namely, the analysis of earthquake data and Twitter data. In an attempt to explain the frequency of events by the spatial location of the data source, both studies use the location as covariates of the sources.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 199-212
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Regression function and noise variance tracking methods for data streams with concept drift
    Autorzy:
    Jaworski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/329716.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    data stream
    concept drift
    Parzen kernel
    regression function
    variance estimation
    strumień danych
    funkcja regresji
    estymacja wariancji
    Opis:
    Two types of heuristic estimators based on Parzen kernels are presented. They are able to estimate the regression function in an incremental manner. The estimators apply two techniques commonly used in concept-drifting data streams, i.e., the forgetting factor and the sliding window. The methods are applicable for models in which both the function and the noise variance change over time. Although nonparametric methods based on Parzen kernels were previously successfully applied in the literature to online regression function estimation, the problem of estimating the variance of noise was generally neglected. It is sometimes of profound interest to know the variance of the signal considered, e.g., in economics, but it can also be used for determining confidence intervals in the estimation of the regression function, as well as while evaluating the goodness of fit and in controlling the amount of smoothing. The present paper addresses this issue. Specifically, variance estimators are proposed which are able to deal with concept drifting data by applying a sliding window and a forgetting factor, respectively. A number of conducted numerical experiments proved that the proposed methods perform satisfactorily well in estimating both the regression function and the variance of the noise.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 3; 559-567
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid cascade neuro-fuzzy network with pools of extended neo-fuzzy neurons and its deep learning
    Autorzy:
    Bodyanskiy, Yevgeniy V.
    Tyshchenko, Oleksii K.
    Powiązania:
    https://bibliotekanauki.pl/articles/330840.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    data stream
    membership function
    training procedure
    adaptive neuro-fuzzy system
    extended neo-fuzzy neuron
    strumień danych
    funkcja przynależności
    neuronowo rozmyty układ adaptacyjny
    Opis:
    This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 477-488
    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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