Sentence examples for automatic approximation from inspiring English sources

Further, our proposed hardware version of the PPI algorithm can significantly outperform (in terms of computation time) the original (semisupervised) version of the algorithm, available in commercial software, a (fully automatic) approximation of the algorithm, and a recently developed FPGA implementation developed for a Xilinx Virtex-II XC2V6000-6 FPGA.

In the present paper, manual (direct), semi-automatic (indirect determination of characteristic points by approximation) and automatic (3D model) methods for extracting characteristic points are presented, along with the results of an accuracy assessment.

The main objective of this contribution is the automatic development of rational approximations of ideal time delay transfer functions resulting in lumped systems of specified finite dimension.

Our evaluation is based on the comparison of the automatic segmentation results with elliptic approximation of manual segmentations.

The method uses an iterative robust two-step spline approximation with an automatic model order determination procedure.

Then we show why the idea of representing a time-series with a fixed number of piece-wise linear segments as done in SAX is not directly applicable to our situation and propose a data-driven solution (Section "Automatic determination of piecewise aggregate approximation").

The derivatives computed by automatic differentiation are compared with approximations based on divided differences.

Although automatic tools do provide a first approximation of the network structure, human analysis remains necessary for curating all the relevant information.

The biological to mathematical mapping allows for separate use of biological and mathematical components, and includes automatic mathematical simplification using pseudo-steady approximations and mass conservation relationships.

The proposed method is fully automatic and does not use likelihood approximations to find the optimal network that explains observed experimental data.

The optimization approach also provides clear convergence criteria for posterior approximation and facilitates model selection through automatic evaluation of the marginal likelihood.