The phrase "average recognition of" is correct and usable in written English.
It can be used when discussing the general acknowledgment or awareness of a particular subject, concept, or entity among a group of people.
Example: "The average recognition of the brand among consumers has increased significantly over the past year."
Alternatives: "general awareness of" or "overall acknowledgment of".
Figure 6 Average recognition rates of STL TSB-HMMs versus the number of training samples for time domain feature and spectrogram feature.
Results show that the average recognition accuracy of SRDSAN is higher than that of the SR and the convolutional neural network.
Fig. 3 The average recognition rates of three methods versus the number of testing samples.
In the second segment of the experiment, we recorded an average recognition performance of 88.9%, which is marginally less than the result in the first segment.
The performance recorded here is slightly less than that of the first segment with the average recognition rate of 88.9%; this short fall can be attributed to the different landmark methods used in labeling the two databases.
Fig. 2 The average recognition rates of two methods (MMC and OMMPS) versus dimensionality of subspace.
Compared with the average recognition rate of original spectrogram feature in Figure 14, that of power transformed spectrogram feature shown in Figure 17 are much larger, especially for small training data sets.
At the end of the first experiment, we achieved an average recognition accuracy of 92.2%; for the seven facial expression targeted neutral, happy, sad, angry, fear, disgust, and surprise with the highest recognition of 98.7 and 97.6% coming from the surprise and happy expressions, respectively, while the lowest recognition of 85.5 and 86.8% came from sad and angry, respectively.
Note that when we only use 2 × 135 samples for training, the average recognition rate of STL TSB-HMMs is only 52.7%, while the average recognition rate of MTL TSB-HMMs is 88.0%.
