نتایج جستجو برای: unconstrained mse
تعداد نتایج: 14977 فیلتر نتایج به سال:
In this paper, we tackle the problem of measuring similarity among graphs that represent real objects with noisy data. To account for noise, we relax the definition of similarity using the maximum weighted co-k-plex relaxation method, which allows dissimilarities among graphs up to a predetermined level. We then formulate the problem as a novel quadratic unconstrained binary optimization proble...
Ridge regression is often favored in the analysis of ill-conditioned systems. A canonical form identifies regions in the parameter space where Ordinary Least Squares (OLS) is problematic. The objectives are two-fold: To reexamine the view that ill-conditioning necessarily degrades essentials of OLS; and to reassess ranges of the ridge parameter k where ridge is efficient in mean squared error (...
Local models and methods construct function estimates or predictions from observations in a local neighborhood of the point of interest. The bandwidth, i.e., how large the local neighborhood should be, is often determined based on asymptotic analysis. In this report, an alternative, non-asymptotic approach that minimizes a uniform upper bound on the mean square error for a linear or affine esti...
Every program must continuously evolve, or it will become obsolete. This paper explores a methodology for software evolution within the setting of object-orientated programming. The methodology is based on the top–down propagation of change, and it is remotely related to stepwise refinement. To present the methodology, this paper uses one small example (Gregorian calendar) and one medium-sized ...
This paper develops a new efficient estimator for the average treatment effect, if selection for treatment is on observables. The new estimator is linear in the first-stage nonparametric estimator. This simplifies the derivation of the means squared error (MSE) of the estimator as a function of the number of basis functions that is used in the first stage nonparametric regression. We propose an...
This paper presents the adaptation of a single layer complex valued neural network (NN) to use entropy in the cost function instead of the usual mean squared error (MSE). This network has the good property of having only one layer so that there is no need to search for the number of hidden layer neurons: the topology is completely determined by the problem. We extend the existing stochastic MSE...
The sun spot time series is a random series generated with a complicated nonlinear system. Time delay neural networks (TDNN) are typical nonlinear systems that could be used to perform time series prediction. In this project, we applied the TDNN trained with both MSE and MEE criteria to predict the time series. Both criteria showed the effectiveness of the nonlinear systems. Furthermore, the TD...
Multiscale entropy (MSE) analysis was introduced in the 2002 to evaluate the complexity of a time series by quantifying its entropy over a range of temporal scales. The algorithm has been successfully applied in different research fields. Since its introduction, a number of modifications and refinements have been proposed, some aimed at increasing the accuracy of the entropy estimates, others a...
نمودار تعداد نتایج جستجو در هر سال
با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید