Sentence examples for robustness of machine from inspiring English sources

Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed.

Fig. 5 Robustness of machine learning methods to different levels of noise for 4 out of 7 activity classes.

Hence, we explored robustness of machine learning algorithms to noise by introducing artificial noise to the datasets and measuring the effect on the performance of the data mining techniques.

Hence, we explored the robustness of machine learning algorithms included in this study by introducing different levels of artificial noise to the datasets and measuring the performance of each method (Table 1).

Hence, the ability of transfer learning would greatly improve the robustness of machine learning models by transferring and adapting knowledge learned from one domain to another related, but different domain.

When examining robustness of machine learning algorithms to different levels of noise it was observed that non-linear methods overall outperformed Naïve Bayes when dealing with low levels of noise, lower or equal to 20%, where performance was retained equal to or higher than MCC of 0.7 on average.

High value of correlation coefficient, i.e., 0.97338 proves the robustness of committee machine modeling Open image in new window Fig. 3 Cumulative probability of error distribution for committee machine model meant to estimate saturation pressure.

Finite element analysis was also performed to verify the static and dynamic robustness of the machine structure.

In order to enhance structural robustness of microfactory machine elements the foam-composite sandwich structure was introduced.

Using a supply chain network, we demonstrate the feasibility, viability, and robustness of applying machine learning and genetic algorithms to respectively model, understand, and optimize such data intensive environments.

As a summary, the applied technique allows clarifying the design of the promotional sales models for a real database (milk category), according to the influence of the figure of merit used for design parameters selection, showing the robustness of the machine learning techniques in this setting.