Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H2-norm-based reduced parameter modeling technique.
Little is known, however, about the sample size that is needed to generate a prediction model with a modern modelling technique that outperforms more traditional, regression-based modelling techniques in medical data.
Among the compared methods they noticed that non-linear party modelling techniques, such as neural network based ones, outperform the linear methods like Mahalanobis.
We used non-spatial modelling techniques.
While this may be common in medical research, it limited the ability for some modern modelling techniques to outperform traditional logistic regression modelling.
Our technique outperforms classical models for all the places.
In terms of computational cost, the proposed technique outperforms the conventional approach that exclusively uses the high-fidelity model to create the surrogates.
Comparative results for narrowband and broadband noise signals show that the proposed technique outperforms other state-of-the-art methods in terms of model accuracy and convergence rate.
Clearly, the graph-based technique outperforms the greedy one.
