Examples of Classification Error in a sentence
We also report the stan- dardized ISO/IEC 30107-3 metrics [52], Attack Presen- tation Classification Error Rate (APCER), and Bonafide Presentation Classification Error Rate (BPCER) in the test set at the previously optimized threshold.To summarize the performance in a single number, the Average Classification Error Rate (ACER) is used, which is an average of APCER and BPCER.
The Insurer shall calculate such Additional Classification Error Correction in accordance with the methodologies, [ *** ] (including methodologies relating to [ *** ]), that were applied by the Insurer in calculating the original Non-Solicited Annuity Premium, the Solicited Annuity Premium, and the Solicited Adjusted Annuity Premium, as applicable.
The development set (Dev) was used to tune all the parameters: the learning rate, the batch size and the hidden layers sizes of MLP-MS, and the features template of CRF, that describes which features are used in training and testing.The performance is evaluated by using recall (R), precision (P) and F- measure (F) for the misrecognized word prediction and global Classification Error Rate (CER).
The Average Classification Error for each of the best subsets is shown on the right, and the optimal feature subset is highlighted in grey.
These other methods of training are the Maximum Mutual Information (MMI) training and the Minimum Classification Error Training (MCE) training.
This unsurprisingly low error rate reflects the fact that high−level vs rising5 0 −5 5 10 15 20 high−level vs falling5 0 −5 5 10 15 20 5 high−level vs low−level0 −5 5 10 15 20 rising vs low−level5 0 −5 5 10 15 20Table 1: Classification Error Rates for Four-way Tone Recog- nition with a Linear SVM in Mandarin for differing conditions of focus.
The development set (Dev) was used to tune all the parameters: the learning rate, the batch size and the hidden layers size of MLP-MS, and the features template of CRF, that describes which features are used in training and testing.The performance is evaluated by using recall (R), precision (P) and F-measure (F) for the misrecognized word prediction and global Classification Error Rate (CER).
We use standard FAS metrics to mea- sure the performance, which are Attack Presentation Classification Error Rate (APCER), Bona Fide Presentation Classification Error Rate (BPCER), and Average Classification Error Rate ACER [1], Receiver Operating Characteristic (ROC) curve.Implementation Details We use Tensorflow [2] in implementation, and we run ex- periments on a single NVIDIA TITAN X GPU.
This is known as Minimum Classification Error (MCE) train- ing, or Discriminative Training.
Misclassification Due to New Classification Standard or Correction of Classification Error.