Sentence examples for classes samples from inspiring English sources

Experiments based on ten datasets containing different numbers of classes, samples, and dimensions are examined.

After the first clustering, according to the distance between classes, samples Fault2_1 to Fault2_4 were merged into a cluster, samples Normal_1 to Normal_4 were merged into another cluster, and samples Fault1_1 to Fault1_4 were merged into the third cluster.

If there are c classes samples, the classifier in level l (1 ≤ l ≤ L) can get posterior probability output of each class shown by P l = {p1, p2,...p c }.

After the first clustering, according to the distance between classes, samples B_1 to B_4 and samples O_1 to O_4 were merged into a cluster, samples I_1 to I_4 were merged into another cluster, and samples N_1 to N_4 were merged into the third cluster.

In particular, two patient subgroups were consistently identified across all 3 ~ 15 classes (samples denoted by light blue and green in Fig.  1b).

According to the cryocrit values obtained, samples were divided into three classes: samples with cryocrit values higher than 1.0%, with cryocrit values between 0.5 and 1.0%, and with absence of cryoglobulins.

This example shows a PUMF with 4 equivalence classes sampled from the DAD population data set with 5 equivalence classes.

However, the existing K-SVD algorithm is employed to dwell on the concept of a binary class assignment, which means that the multi-classes samples are assigned to the given classes definitely.

The Positive subset contained the majority class samples where its neighbors have the sample class label, the Minority subset contained the minority samples, and the Boundary subset contained the minority samples that have any majority class sample in its neighbors.

The final model is then used to predict the class samples of the test set.

The results indicate that the first seven stages rejected 98%% of the non-current class samples.