Meta-learning recommends the best multi-target regression method using 58 meta-features from 648 synthetic datasets, with Random Forest achieving over 70% balanced accuracy.
Multi-target regression via input space expansion: treating targets as inputs
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2019 2verdicts
UNVERDICTED 2representative citing papers
A hierarchical framework learns generalisable coupling terms for bounded reactive obstacle avoidance by unifying perception, decision and action via low-dimensional geometric descriptors and dynamic movement primitives.
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Towards meta-learning for multi-target regression problems
Meta-learning recommends the best multi-target regression method using 58 meta-features from 648 synthetic datasets, with Random Forest achieving over 70% balanced accuracy.
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Learning Generalisable Coupling Terms for Obstacle Avoidance via Low-dimensional Geometric Descriptors
A hierarchical framework learns generalisable coupling terms for bounded reactive obstacle avoidance by unifying perception, decision and action via low-dimensional geometric descriptors and dynamic movement primitives.