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metadata

EBM
Data which describe other data, especially containing XML tags, characterising attributes of values in clinical data fields.
References in periodicals archive ?
To deal with the scarcity of labeled data, they used data clustering for partitioning unlabeled micro-blogs and selecting randomly some micro-blogs from the resulted groups.
With labeled data, SVM learns a boundary (i.e., hyperplane) separating different class data with maximum margin.
For prediction purpose, a supervised layer should be added above the DBN to adjust the learned features by labeled data using an up-down fine-tuning algorithm.
In their work, the term weakly labeled data is presented for biomedical relation extraction from MEDLINE.
In fact, in SS_RCE, random correlation terms between within-class samples are used to extract diverse correlated features between different views, and component classifiers are trained based on the diverse correlated features of labeled data. Due to the creation of random subsets by resampling of all train data (both labeled and unlabeled samples) and preserving discriminative information by incorporating within-class correlation terms corresponding to labeled and unlabeled data, we can create accurate and diverse component classifiers on the extracted features and final all component classifiers are combined to make predictions.
Supervised learning methods need a large amount of labeled data to train the classifier.
* If S is empty, use the predictor f trained from all labeled data.
In heterogenous transfer learning, besides data pairs [mathematical expression not reproducible] which come from the source and target domain, we always can collect a set of target domain unlabeled data [mathematical expression not reproducible], and a set of source domain labeled data [mathematical expression not reproducible].
However, as we discussed in [10] more research is required that should take into consideration the experts' advice using the labeled data along with the capability of using unlabeled data due to the high costs and time involved in getting labeled datasets.
It only requires a small quantity of labeled data and some unlabeled data to fulfill the requirements for precise classification.
2006) is a methodology applied to a collection of data containing a single labeled data set [X.sub.l] = {[x.sub.1], [x.sub.2], ..., [x.sub.l]} [member of] [R.sup.d] (the associated label set is assumed to be [Y.sub.l] = {[y.sub.1], [y.sub.2], ..., [y.sub.l]} [member of] R), and u-unlabeled dataset [X.sub.u] = {[x.sub.l+1], [x.sub.l+2], ..., [x.sub.l+u]} [member of] [R.sup.d], to improve the accuracy of prediction by supervised learning based on labeled data by using the information supplied by unlabeled data as well.