"canonical performance" is a correct and usable phrase in written English.
It is used to describe a performance that closely follows accepted norms and conventions. For example, "He gave a canonical performance of the Shakespeare play, ensuring that every line was delivered exactly how it was written by the Bard."
Furthermore, KG-SCCA could accurately recover the true signals from the synthetic data, as well as yield improved canonical correlation performances and biologically meaningful findings from real data.
Another interesting point might be seen in Figure 9. Adding the sixth component to KLT (and also Fourier) canonical representation, the performance is slightly worse than the five-component approximated message.
Technically virtuosic and majestically expressive, she brought new dynamism to the performance of canonical roles and changed the way classics were danced, showing it was possible to dance 19th-century ballet in the modern world.
A canonical discriminant analysis of performance metrics revealed large differences between the four systems.
In[7, 8], it has been shown that, compared to conventional distributed turbo coding and separate network and channel decoding, JNCD can improve the performance of canonical two-way and multiple-access relay channels.
That point is reflected in Carnegie Hall's offerings, which include Mr. Gil's tribute to Brazil's rural forró tradition; examples of contemporary Afro-Cuban jazz; a starry concert honoring Chavela Vargas, a storied Mexican singer who died in August; and performances of canonical and contemporary Latin American classical works.
Given the finding that adding features from both D-MIAT and canonical discretization algorithms helps improve performance, we hypothesized that the combination of these features would be effective to achieve the best performance as per research question #4.
In order to improve the performance of the canonical decision tree, this paper proposes the soft binary decision tree structure which is able to establish several fuzzy paths from the root to multiple leaves.
The performance of the canonical model in recovering positive RNAi hits was compared to that of the function-specific model (Figure 5).
The performance of a canonical supervised machine-learning model, trained on the same prior features and training space we used for our function-specific classifier model and differing in that it aggregates training data for all GO terms, is shown for comparison (Figure 5).
