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Dynamic Instance-Wise Classification in Correlated Feature Spaces
In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training.
On-the-Fly Joint Feature Selection and Classification
Specifically, online feature selection methods can handle either streaming features or data instances offline to produce a fixed set of features for classification, while online classification methods classify incoming instances using full knowledge about the feature space.
Not all Embeddings are created Equal: Extracting Entity-specific Substructures for RDF Graph Embedding
However, such approaches lead to representations comprising mostly "popular", instead of "relevant", entities in the KG.
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