A domain adaptation method is adopted to minimize the maximum mean discrepancy between training and testing data.
We introduce two novel approaches outperforming state-of-the-art algorithms when tested on the KTH and Weizmann public datasets: an unsupervised nonparametric kernel-based method exploiting the Maximum Mean Discrepancy test statistic; and a supervised method based on Support Vector Machine with a characteristic kernel specifically tailored to histogram-based information.
The preliminary results show good agreement between the computed profiles and the ABL wind-tunnel simulations, as the mean discrepancy between the experimental and computational results is 4.5% for the mean velocity profile, 2.5% for the Reynolds shear stress profile, 6% for the turbulent kinetic energy profile.
Values in this table measure the mean discrepancy in estimated effects and prediction MSE.
Highly undetectable stego (HUGO) [5], ASO [17], universal wavelet relative distortion (UNIWARD) [18], and maximum mean discrepancy (MMD) [19] are designed on this principle.
The mean discrepancy between correlation coefficients for participants and non-participants were 0.05 and 0.04 at one-year and 15-year follow-up, respectively.
Romay et al. (2013) compared GBS-SNP calls with Illumina array-based genotype values and estimated a mean discrepancy rate of 0.0118 between these two genotyping platforms.
We review related literature in Section 2. In Section 3 we illustrate Maximum Mean Discrepancy, which is the core of our unsupervised method, and review the definition of characteristic kernels.
All measurements were performed by the same investigator, and the accelerating voltage was fixed at 1 kV, 5(b), and 5(c)) Measurements for each FDP were averaged, and these were used to determine the mean discrepancy in the marginal fit in each group (n = 20).
At the last feature and classification layer of each autoencoder network, the marginal and conditional distributions are matched by introducing a Maximum Mean Discrepancy (MMD) metric.
