Classification

Time series feature learning with labeled and unlabeled data
Time series classification has attracted much attention in the last two decades. However, in many real-world applications, the …
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Multi-instance learning with discriminative bag mapping
Multi-instance learning (MIL) is a useful tool for tackling labeling ambiguity in learning because it allows a bag of instances to …
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Positive and unlabeled multi-graph learning
In this paper, we advance graph classification to handle multi-graph learning for complicated objects, where each object is represented …
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SODE: Self-adaptive one-dependence estimators for classification
SuperParent-One-Dependence Estimators (SPODEs) represent a family of semi-naive Bayesian classifiers which relax the attribute …
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Finding the best not the most: regularized loss minimization subgraph selection for graph classification
Classification on structure data, such as graphs, has drawn wide interest in recent years. Due to the lack of explicit features to …
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Locally weighted learning: how and when does it work in Bayesian networks?
Bayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic …
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Ensemble of multiple descriptors for automatic image annotation
Automatic image annotation (AIA) plays an important role and attracts much research attention in image understanding and retrieval. …
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