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Kernel equalizer
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Part 1 of ebook "Certified programming with dependent types" provides readers with contents including: basic programming and proving; programming with dependent types; inductive predicates; infinite data and proofs; subset types and variations; general recursion; more dependent types; dependent data structures; reasoning about equality proofs;...
251p
tieulangtran
28-09-2023
4
1
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Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance of predictor variables.
18p
vicoachella2711
27-10-2020
16
0
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In this paper, we propose the use of weighted categorical kernel functions to predict drug resistance from virus sequence data. These kernel functions are very simple to implement and are able to take into account HIV data particularities, such as allele mixtures, and to weigh the different importance of each protein residue, as it is known that not all positions contribute equally to the resistance.
13p
vicolorado2711
23-10-2020
11
0
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In previous articles, we proposed single kernel and multikernel equalizers for nonlinear satellite channels with significant improvements in performance. The results demonstrated that the advantages of kernel equalizers over radius basis function neural equalizers are the ability to achieve overall convergence, which results in smaller output errors.
5p
vishizuka2711
07-04-2020
17
3
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The combination of the kernel trick and the least-mean-square (LMS) algorithm provides an interesting sample by sample update for an adaptive equalizer in reproducing Kernel Hilbert Spaces (RKHS), which is named here the KLMS. This paper shows that in the finite training data case, the KLMS algorithm is well-posed in RKHS without the addition of an extra regularization term to penalize solution norms.
4p
sabiendo
06-02-2020
12
0
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This paper proposes a new approach to combine the convex of two single-kernel adaptive equalizers with different convergent rates and different efficiencies in order to get the best kernel equalizer. This is the Gaussian multikernel equalizer.
5p
vititan2711
13-08-2019
7
0
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We generate a large number of predictive models by applying linear kernel SVR to historical currency rates’ bid data for three currency pairs obtained from high-frequency trading. The bid tick data are converted into equally spaced (1 min) data. Differences of price between the previous successive timeframes are used as features to predict the direction of movement of the price in the next timeframe.
10p
vithanos2711
09-08-2019
24
0
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An inequality is proved for a quadratic functional with the logarithmic kernel. The best constant of this inequality and the corresponding function for which the equality holds are found precisely.
4p
thienthanquydu
23-10-2018
27
1
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