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Wyszukujesz frazę "reduction of dimension" wg kryterium: Temat


Wyświetlanie 1-3 z 3
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:
A comparative study for outlier detection methods in high dimensional text data
Autorzy:
Park, Cheong Hee
Powiązania:
https://bibliotekanauki.pl/articles/2201316.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
curse of dimensionality
dimension reduction
high dimensional text data
outlier detection
Opis:
Outlier detection aims to find a data sample that is significantly different from other data samples. Various outlier detection methods have been proposed and have been shown to be able to detect anomalies in many practical problems. However, in high dimensional data, conventional outlier detection methods often behave unexpectedly due to a phenomenon called the curse of dimensionality. In this paper, we compare and analyze outlier detection performance in various experimental settings, focusing on text data with dimensions typically in the tens of thousands. Experimental setups were simulated to compare the performance of outlier detection methods in unsupervised versus semisupervised mode and uni-modal versus multi-modal data distributions. The performance of outlier detection methods based on dimension reduction is compared, and a discussion on using k-NN distance in high dimensional data is also provided. Analysis through experimental comparison in various environments can provide insights into the application of outlier detection methods in high dimensional data.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 1; 5--17
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effective energy integral functionals for thin films with three dimensional bending moment in the Orlicz-Sobolev space setting
Autorzy:
Laskowski, Włodzimierz
Nguyen, Hong
Powiązania:
https://bibliotekanauki.pl/articles/729638.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:
Γ-convergence
3D-2D dimension reduction
quasiconvex relaxation
minimizers of variational integral functionals
thin films
elastic membranes
effective energy integral functional
bulk and surface energy
equilibrium states of the film
non-power-growth-type bulk energy density
reflexive Orlicz and Orlicz-Sobolev spaces
Opis:
In this paper we consider an elastic thin film ω ⊂ ℝ² with the bending moment depending also on the third thickness variable. The effective energy functional defined on the Orlicz-Sobolev space over ω is described by Γ-convergence and 3D-2D dimension reduction techniques. Then we prove the existence of minimizers of the film energy functional. These results are proved in the case when the energy density function has the growth prescribed by an Orlicz convex function M. Here M is assumed to be non-power-growth-type and to satisfy the conditions Δ₂ and ∇₂.
Źródło:
Discussiones Mathematicae, Differential Inclusions, Control and Optimization; 2016, 36, 1; 7-31
1509-9407
Pojawia się w:
Discussiones Mathematicae, Differential Inclusions, Control and Optimization
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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