Sentence examples for target instances from inspiring English sources

A learner is now trained with the transformed labeled target instances.

This process also preserves the structural consistency between the labeled and unlabeled target instances.

This method exploits the knowledge from unlabeled target instances to enhance a target HTL task with limited target labels.

Then, the counter ct is incremented in each of these three target instances (lines 3, 4 and 5).

Then, the misclassified source instances are lowered in importance and the misclassified target instances are raised in importance.

During manifold regularization, unlabeled target instances are used to reduce overfitting issues caused by having very limited labeled target data.

Given a schema mapping, we then provide an algorithm to construct a canonical target instance.

The higher the β, the more similar an unlabeled target instance is.

This article investigates the construction of target instance for XML data exchange, which has received far less attention.

Using Pearson correlation [20], the similarity is measured between the new arriving target instance and its co-occurred counterpart.

Moreover, we develop techniques to enforce non-key constraints on the canonical target instance, by providing a chase method to reason about data.