• Title/Summary/Keyword: kernels

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TL-FINITE STATE MACHINES OVER FINITE GROUPS

  • Cho, Sung-Jin
    • Journal of applied mathematics & informatics
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    • v.8 no.3
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    • pp.1009-1019
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    • 2001
  • We introduce the concepts of TL-finite state machine, TL-kernel and TL-subfinite state machines, TL-kernel and TL-subfinite state machine and obtain some results concerning them.

THE EXACT BERGMAN KERNEL AND THE EXTREMAL PROBLEM

  • Jeong, Moonja
    • Korean Journal of Mathematics
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    • v.13 no.2
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    • pp.183-191
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    • 2005
  • In this paper we find the Laurent series expansions representing the reproducing kernels. Also we find the number of zeroes of the exact Bergman kernel via parallel slit domain in order to relate the exact Bergman kernel to an extremal problem.

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A Note on Support Vector Density Estimation with Wavelets

  • Lee, Sung-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.411-418
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    • 2005
  • We review support vector and wavelet density estimation. The relationship between support vector and wavelet density estimation in reproducing kernel Hilbert space (RKHS) is investigated in order to use wavelets as a variety of support vector kernels in support vector density estimation.

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COMPUTATION OF THE MATRIX OF THE TOEPLITZ OPERATOR ON THE HARDY SPACE

  • Chung, Young-Bok
    • Communications of the Korean Mathematical Society
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    • v.34 no.4
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    • pp.1135-1143
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    • 2019
  • The matrix representation of the Toeplitz operator on the Hardy space with respect to a generalized orthonormal basis for the space of square integrable functions associated to a bounded simply connected region in the complex plane is completely computed in terms of only the Szegő kernel and the Garabedian kernels.

Multi-User Detection using Support Vector Machines

  • Lee, Jung-Sik;Lee, Jae-Wan;Hwang, Jae-Jeong;Chung, Kyung-Taek
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.12C
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    • pp.1177-1183
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    • 2009
  • In this paper, support vector machines (SVM) are applied to multi-user detector (MUD) for direct sequence (DS)-CDMA system. This work shows an analytical performance of SVM based multi-user detector with some of kernel functions, such as linear, sigmoid, and Gaussian. The basic idea in SVM based training is to select the proper number of support vectors by maximizing the margin between two different classes. In simulation studies, the performance of SVM based MUD with different kernel functions is compared in terms of the number of selected support vectors, their corresponding decision boundary, and finally the bit error rate. It was found that controlling parameter, in SVM training have an effect, in some degree, to SVM based MUD with both sigmoid and Gaussian kernel. It is shown that SVM based MUD with Gaussian kernels outperforms those with other kernels.