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A tutorial on multilabel learning
Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …
increasing number of fields where it can be applied and also to the emerging number of …
Mining multi-label data
A large body of research in supervised learning deals with the analysis of single-label data,
where training examples are associated with a single label λ from a set of disjoint labels L …
where training examples are associated with a single label λ from a set of disjoint labels L …
[HTML][HTML] Automatic cell-type harmonization and integration across Human Cell Atlas datasets
Harmonizing cell types across the single-cell community and assembling them into a
common framework is central to building a standardized Human Cell Atlas. Here, we present …
common framework is central to building a standardized Human Cell Atlas. Here, we present …
A survey of hierarchical classification across different application domains
In this survey we discuss the task of hierarchical classification. The literature about this field
is scattered across very different application domains and for that reason research in one …
is scattered across very different application domains and for that reason research in one …
Decision trees for hierarchical multi-label classification
Hierarchical multi-label classification (HMC) is a variant of classification where instances
may belong to multiple classes at the same time and these classes are organized in a …
may belong to multiple classes at the same time and these classes are organized in a …
Multi‐label learning: a review of the state of the art and ongoing research
Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …
to improve performance in problems where a pattern may have more than one associated …
[PDF][PDF] Effective and efficient multilabel classification in domains with large number of labels
This paper contributes a novel algorithm for effective and computationally efficient multilabel
classification in domains with large label sets L. The HOMER algorithm constructs a …
classification in domains with large label sets L. The HOMER algorithm constructs a …
Tree ensembles for predicting structured outputs
In this paper, we address the task of learning models for predicting structured outputs. We
consider both global and local predictions of structured outputs, the former based on a …
consider both global and local predictions of structured outputs, the former based on a …
On machine-learned classification of variable stars with sparse and noisy time-series data
With the coming data deluge from synoptic surveys, there is a need for frameworks that can
quickly and automatically produce calibrated classification probabilities for newly observed …
quickly and automatically produce calibrated classification probabilities for newly observed …
[HTML][HTML] AI applications in functional genomics
We review the current applications of artificial intelligence (AI) in functional genomics. The
recent explosion of AI follows the remarkable achievements made possible by “deep …
recent explosion of AI follows the remarkable achievements made possible by “deep …