Sentence examples for marginal distribution between from inspiring English sources

The final classifier learning process minimizes the structural risk functional [117] and the marginal distribution between domains using the maximum mean discrepancy measure [10].

The second approach discovers underlying meaningful structures between the domains to find a common latent feature space that has predictive qualities while reducing the marginal distribution between the domains (e.g. Blitzer [5]).

The surveyed works of Duan [27], Gong [42], Pan [87], Li [62], Shi [106], Oquab [81], Glorot [41], and Pan [83] are focused on solving the differences in marginal distribution between the source and target domains.

To address the difference in marginal distribution between the domains, the maximum mean discrepancy distance measure [10] is used to compute the marginal distribution differences and is integrated into the PCA optimization algorithm.

This is referred to as frequency feature bias and will cause the marginal distribution between the source and target domains to be different (left( {{text{P}}({text{X}}_{text{S}} ), ne,{text{P}}left( {{text{X}}_{text{T}} } right)} right)text).

This transfer learning framework proposes to correct the difference in marginal distribution between the source and target domains, correct the difference in conditional distribution between the domains, and improve classification performance through a manifold regularization [4] process (which optimally shifts the hyperplane of an SVM learner).

Both the Pan [87] and Gopalan [43] methods are two-stage domain adaptation processes where the first stage reduces the marginal distributions between the domains and the second stage trains a classifier with the adapted domain data.

A difference in marginal distributions between positively and negatively correlated transcripts is clear.

This examines whether the marginal distributions between raters are systematically different from each other.

This, in effect, reduces the marginal distribution differences between the domains in the subspace.

This is referred to as frequency feature bias, which causes marginal distribution differences between the domains.