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    Wyświetlanie 1-4 z 4
    Tytuł:
    An approximate necessary condition for the optimal bandwidth selector in kernel density estimation
    Autorzy:
    Gajek, L.
    Lenic, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/1340710.pdf
    Data publikacji:
    1993
    Wydawca:
    Polska Akademia Nauk. Instytut Matematyczny PAN
    Tematy:
    bandwidth selection
    kernel density estimation
    resampling
    Opis:
    An approximate necessary condition for the optimal bandwidth choice is derived. This condition is used to construct an iterative bandwidth selector. The algorithm is based on resampling and step-wise fitting the bandwidth to the density estimator from the previous iteration. Examples show fast convergence of the algorithm to the bandwidth value which is surprisingly close to the optimal one no matter what is the initial knowledge on the unknown density.
    Źródło:
    Applicationes Mathematicae; 1993-1995, 22, 1; 123-138
    1233-7234
    Pojawia się w:
    Applicationes Mathematicae
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    FPGA-based bandwidth selection for kernel density estimation using high level synthesis approach
    Autorzy:
    Gramacki, A.
    Sawerwain, M.
    Gramacki, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/201258.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    FPGA
    high level synthesis
    kernel density estimation
    bandwidth selection
    plug-in selector
    synteza wysokiego poziomu
    jądrowy estymator gęstości
    wybór pasma informacyjnego
    Opis:
    Field-programmable gate arrays (FPGA) technology can offer significantly higher performance at much lower power consumption than is available from single and multicore CPUs and GPUs (graphics processing unit) in many computational problems. Unfortunately, the pure programming for FPGA using hardware description languages (HDL), like VHDL or Verilog, is a difficult and not-trivial task and is not intuitive for C/C++/Java programmers. To bring the gap between programming effectiveness and difficulty, the high level synthesis (HLS) approach is promoted by main FPGA vendors. Nowadays, time-intensive calculations are mainly performed on GPU/CPU architectures, but can also be successfully performed using HLS approach. In the paper we implement a bandwidth selection algorithm for kernel density estimation (KDE) using HLS and show techniques which were used to optimize the final FPGA implementation. We are also going to show that FPGA speedups, comparing to highly optimized CPU and GPU implementations, are quite substantial. Moreover, power consumption for FPGA devices is usually much less than typical power consumption of the present CPUs and GPUs.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 4; 821-829
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Graphics processing units in acceleration of bandwidth selection for kernel density estimation
    Autorzy:
    Andrzejewski, W.
    Gramacki, A.
    Gramacki, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/330819.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    bandwidth selection
    graphics processing unit
    probability density function
    nonparametric estimation
    kernel estimation
    szerokość pasmowa
    programowalny procesor graficzny
    funkcja gęstości prawdopodobieństwa
    estymacja nieparametryczna
    estymacja jądrowa
    Opis:
    The Probability Density Function (PDF) is a key concept in statistics. Constructing the most adequate PDF from the observed data is still an important and interesting scientific problem, especially for large datasets. PDFs are often estimated using nonparametric data-driven methods. One of the most popular nonparametric method is the Kernel Density Estimator (KDE). However, a very serious drawback of using KDEs is the large number of calculations required to compute them, especially to find the optimal bandwidth parameter. In this paper we investigate the possibility of utilizing Graphics Processing Units (GPUs) to accelerate the finding of the bandwidth. The contribution of this paper is threefold: (a) we propose algorithmic optimization to one of bandwidth finding algorithms, (b) we propose efficient GPU versions of three bandwidth finding algorithms and (c) we experimentally compare three of our GPU implementations with the ones which utilize only CPUs. Our experiments show orders of magnitude improvements over CPU implementations of classical algorithms.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 869-885
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Bandwidth selection for kernel generalized regression neural networks in identification of hammerstein systems
    Autorzy:
    Lv, Jiaqing
    Pawlak, Mirosław
    Powiązania:
    https://bibliotekanauki.pl/articles/2031118.pdf
    Data publikacji:
    2021
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    generalized regression neural network
    nonparametric estimation
    bandwidth
    data-driven selection
    nonlinear system
    Hammerstein system
    Opis:
    This paper addresses the issue of data-driven smoothing parameter (bandwidth) selection in the context of nonparametric system identification of dynamic systems. In particular, we examine the identification problem of the block-oriented Hammerstein cascade system. A class of kernel-type Generalized Regression Neural Networks (GRNN) is employed as the identification algorithm. The statistical accuracy of the kernel GRNN estimate is critically influenced by the choice of the bandwidth. Given the need of data-driven bandwidth specification we propose several automatic selection methods that are compared by means of simulation studies. Our experiments reveal that the method referred to as the partitioned cross-validation algorithm can be recommended as the practical procedure for the bandwidth choice for the kernel GRNN estimate in terms of its statistical accuracy and implementation aspects.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 3; 181-194
    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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