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metadata

EBM
Data which describe other data, especially containing XML tags, characterising attributes of values in clinical data fields.

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With labeled data, SVM learns a boundary (i.e., hyperplane) separating different class data with maximum margin.

Incremental Support Vector Machine Combined with Ultraviolet-Visible Spectroscopy for Rapid Discriminant Analysis of Red Wine

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.

Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method

In their work, the term weakly labeled data is presented for biomedical relation extraction from MEDLINE.

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

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.

Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation

Supervised learning methods need a large amount of labeled data to train the classifier.

Sentiment classification based on domain prediction

* If S is empty, use the predictor f trained from all labeled data.

Building accurate text classifiers

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].

Instance-Wise Denoising Autoencoder for High Dimensional Data

Traditional classifiers only use labeled data (features/target pairs).

An adaptive Neuro-fuzzy System with semi-supervised learning as an approach to improving data classification: An illustration of bad debt recovery in healthcare

It only requires a small quantity of labeled data and some unlabeled data to fulfill the requirements for precise classification.

An improved classification scheme with adaptive region growing and Wishart classification algorithm for digital images

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.