Sentence examples for classifications when the from inspiring English sources

Standard subtyping methods produced inaccurate classifications when the clinicopathological distributions of the study cohort did not match those of the training cohort.

We show that standard gene centering produces inaccurate molecular subtype classifications when the clinicopathological distribution of the study cohort differs from that of the training cohort.

Although standard gene centering is commonly applied prior to molecular subtyping, it produces inaccurate molecular subtype classifications when the study and training cohorts differ in their clinicopathological composition.

However, standard gene centering introduces errors in molecular subtype classifications when the clinicopathological distributions of the study cohort do not match those of the training cohort used to derive the molecular subtype classifier.

Standard gene centering is routinely done prior to molecular subtyping, but it can produce inaccurate classifications when the distribution of clinicopathological characteristics in the study cohort differs from that of the training cohort used to derive the classifier.

It was found that a sample size of 4000 samples per frame was sufficient for almost perfect detection and classification when the AR poles' distance from the centre of unit circle was used as the fault indicator.

stability of the emotion classification when the gender type of the GMM model is chosen incorrectly;   4.

The values within the main diagonal illustrate the probability of the correct classification when the input class w i is correctly declared as w i.

Some researches [11] mention that transfer learning makes some crashes to recognition or to classification when the source and target data of several domains has large difference.

The last row shows the confusion in patch classification, when the context is not correctly identified (in this case, overestimated) by aspect model 2. We investigate the impact of the combination with spatial regularization on the task of patch classification.

Extensive experiments on seven datasets demonstrate the effectiveness of the LRSMs in music genre, mood, and multi-label classification when the mel-frequency cepstral coefficients (MFCCs), the chroma, and the auditory cortical representations are employed for music representation.