With small number of training data sets, the use of unlabeled weight improves performance significantly with consistency in results.
Further more, its classification accuracy increases with the addition of unlabeled documents, demonstrating effective use of unlabeled data.
Specifically, it focuses on the required gains in computational efficiency, broader classes of loss functions, and the use of unlabeled data to improve statistical efficiency.
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.
For constrained estimation of mixtures of HMMs, we also investigated the use of unlabeled patients during training (HMMConstAll).
We can make use of unlabeled text for feature extractions or build deep belief networks for sparse feature learning.
HRM assays require only the use of unlabeled PCR primers and a single saturating intercalating fluorescent dye (e.g. Eva Green).
This common unlabeled data set (x_{mathrm{ul}}) is used by RBM1 to compute unlabeled weights (W_1) and bias (a_1) and (b_1).
This RBM2 extracts the features from the labeled training data and the use of the unlabeled features as initial weights of the RBM2 reduces the requirement of the labeled training data considerably.
