Sentence examples for model complexity in from inspiring English sources
The role of model complexity in safety analysis is also described.
To reduce computational load and model complexity in product design when using 3D anthropometric data, wavelet analysis is adopted in this paper to establish multi-resolution mathematical description of 3D anthropometric data.
This method reduces storage requirement of the image pyramid to less than 5% of the full image, and computation in this domain reduces model complexity in terms of both computational costs and memory requirements accordingly.
Moreover, a scale factor η is designed for reducing model complexity in CA-CSMWPLS fitness function and a peculiar coding and a decoding rule are adopted in this algorithm.
To test the hypothesis that spatially selective corneal stromal stiffening can alter corneal astigmatism and assess the effects of treatment orientation, pattern, and material model complexity in computational models using patient-specific geometries.
On one hand, this makes it possible to employ advanced sparse solvers to handle large scale problems; on the other hand, a globally optimal subset of basis can be chosen adaptively given desired strength of penalizing model complexity, in contrast to some current endeavors that pre-determine the basis without coupling it with the learning task.
The growing requirement of flexibility particularly on the demand side (e.g. storage or demand side management) additionally increases modelling complexity in systems with high shares of renewables.
Feature selection is essential to remove the redundancy information or noise existing in the extracted features and decrease the models complexity in classification problems [ 23]. (3) The existing method was based on individual classifier, which could have its own inherent defects limiting the prediction performance [ 24].
This leads to further reduction in model complexity and in number of parameters.
Such an analysis allows for a considerable reduction in model complexity, resulting in a semi-analytical model.
This methodology was chosen to reduce overfitting by penalizing "model complexity" implemented in the ridge regression.
