Besides, the incidence of ADEs is most likely underestimated due to underreporting, missed recognition and lack of recording in daily clinical practice.
Missing data recognition has been developed to deal with this challenge.
A vital aspect of missing feature recognition is still the estimation of the feature uncertainty.
Unlike other denoising methods, missing data recognition does not match the whole data with the acoustic models, but instead considers part of the signal as missing, i.e. corrupted by noise.
At first, results are given for the estimated uncertainty values and RASTA-PLP features in Table 3 and for MFCC features in Table 4. Especially for RASTA-PLP features, results are improved notably by masking and missing feature recognition by modified imputation, averaging an absolute improvement of more than 10% over all tested masks and experiments.
Murine natural killer (NK) cells express inhibitory Ly49 receptors for MHC class I molecules, which allows for "missing self" recognition of cells that downregulate MHC class I expression.
A presumed reason for this under-representation would be that NK cells with too high affinity for self MHC class I are excluded from the repertoire, as they would be less sensitive to normal cells with downregulated MHC class I expression (missing self recognition).
The URA5 sequence analysis of the VNII-1 isolates revealed that these isolates are missing the recognition site for Sau96I at position 160, which is a specific, identifying marker of VNI isolates (Figure 5), and the overall sequence similarity of the combined four loci showed that those isolates were more similar to VNI (99.41%) than VNII (98.91%) (Table 4).
The classifying result will be considered as a miss recognition, and the OVR-SVMs classifying model is executed repeatedly until a given Boolean condition (d≤0.2) is met.
