Sentence examples for use of unlabeled data from inspiring English sources

This approach of use of unlabeled data set is also called "self-taught learning" and first proposed by Raina [16].

The use of unlabeled data reduces the requirement of labeled training data and provides high performance with consistency in results.

We design a top-down divisive clustering algorithm that ensures maximal information gain in the use of unlabeled data via clustering similar words.

The proposed algorithm yields encouraging experimental results on a number of image classification problems and demonstrates effective use of unlabeled data.

Table 5 shows that for a given size of training data, the use of unlabeled data provides more accuracy and small SD in accuracy.

It is worth noting that the use of unlabeled data in soft target training is different from that of semi-supervised learning.

This layer employs AdaBoost and Co-Training algorithm to build a robust classifier using large corpus of unlabeled data.

The main advantage of co-training is the fact that we can use the vast amount of unlabeled data (pairs of microarrays for which we do not know if they are similar or not) to improve our classifiers.

Predictions from the teacher network are used as training targets for an easier-to-evaluate student network using a large amount of unlabeled data.

The idea of using an abundant source of unlabeled data available through an internet search to improve prediction performance is a very alluring premise.

Active learning and semi-supervised learning address this problem by using a large amount of unlabeled data together with labeled data to build better models.