The phrase "lead to good performance" is correct and usable in written English.
You can use it when discussing the effect of an action, strategy, or process on the outcome of something. For example, "Implementing a comprehensive customer service plan has been found to lead to good performance in customer satisfaction surveys."
The measurements about the behaviour of the system confirm that the hardware and software solutions proposed do indeed lead to good performance.
To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance.
Based on subjective ratings, task performance, interview feedback, and video analysis, we found that having multiple seamless Interaction Contexts (ICs) with complementary benefits can lead to good performance and user experience, despite the complexity of learning and using the hybrid system.
He reports that Shari'ah screenings do not lead to good performance of Saudi Islamic indices.
Despite the fact that they have exactly the same network topology and human resources, team 1, by organizing itself according to the similarity in expertise of its members, is likely to achieve a better performance than team 2, given that expertise similarity that matches the purpose in a sub-team will lead to good performance.
In this example, CND-based network theories can hardly distinguish the two systems, but if expertise similarity in a sub-team will lead to good performance, then we know that team 1 is better than team 2. Despite the theoretical success of the CND in studying network structure, most realistic case studies of network systems have to consider both topology and node activities simultaneously.
In the literature, several randomized optimization algorithms based on the CE method have been proposed and have been shown to lead to good performances on numerous optimization problems [17 19], often outperforming other randomized algorithms [18].
In addition, since it was shown that the ML decoder for embedding in the magnitude of the DFT domain is sensitive to watermark amplitude, we hope that the sub-optimal decoders in this domain could decrease this sensitivity and lead to good performances in the presence of additional noise.
In most cases, these functions are derived by least-squares fitting which usually leads to good performance indices, even when based on a limited range of data that especially lack high flow observations.
On the other hand, fixed thresholds set to low values are less sensitive to performance degradation under low load or high mobility; however, under high load, they are more consistent with actual link qualities and thus give rise to a low number of route recoveries, which leads to good performance.
Their study indicated that the use of TMM method led to good performance on simulated data sets.
