Sentence examples for maximum entropy regularization from inspiring English sources

In order to improve the occultation results, we introduce in this paper two nonlinear methods, namely the regularization method and the maximum entropy regularization method.

Through designed simulative experiments, we verify and compare these three methods, and conclude that the maximum entropy regularization method can reduce significantly the influence of measurement errors.

As a practical tool for the first step of data analysis, we describe how the boundary resolution of the sedimentation coefficient distribution c(s) can be further improved with a Bayesian adjustment of maximum entropy regularization to the case of heterogeneous interactions between molecules that have been previously studied separately.

Alternatively, we obtained distributions by non-negative least squares fitting with an additional maximum entropy regularization term.

The sedimentation coefficient distribution c(s) was calculated with maximum entropy regularization at a confidence level of p = 0.7 and at a resolution of sedimentation coefficients of n = 100.

(30) The distribution was discretized with a grid of 100 150 s values from 0 to 15 S, and maximum entropy regularization at a confidence level of P = 0.68 was used to produce the broadest distribution consistent with the data.

This paper is a synthetic overview of regularization, maximum entropy and probabilistic methods for some inverse problems such as deconvolution and Fourier synthesis problems which arise in mass spectrometry.

The local maximum entropy approach is easily applicable to the continuum regularization of fluctuating membranes, and the prediction of membrane and bending elasticities of molecular dynamics models.

Conventional, soft-partition clustering approaches, such as fuzzy c-means (FCM), maximum entropy clustering (MEC) and fuzzy clustering by quadratic regularization (FC-QR), are usually incompetent in those situations where the data are quite insufficient or much polluted by underlying noise or outliers.

Once normalized as V (t ) = u (t ) u (0 ), it serves as a typical form of DEER data presentation, while  u(t -1 gives background free dat -1which was subsequently converted to a distance distribution between spin pairs with L-curve Tikhonov regivesization (Chiang et al., 2005a) followed, when needed, background entropy method refreement (Chiang et al., 2005b).

Alternatively, we used the MemSys5 quantified maximum entropy algorithm [18], modified so that a cross-validation statistic determined the stopping value of the regularization coefficient [19] [20].