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Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning

AAAI 2017 · Label-efficient and robust learning › Recognition and long-tail · approach: Partial and multi-label learning · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use MIMLperf instead of MIMLSVMMIMLSVM · Scene linearF-macro: 0.4292 -> 0.5427Helped
use MIMLperf instead of MIMLSVMMIMLSVM · Reuters RBFaverage precision: 0.9467 -> 0.9706Helped
use MIMLperf instead of M3MIMLM3MIML · Reuters RBFF-micro: 0.7866 -> 0.8942Helped
use MIMLperf instead of MM-FβMM-Fβ · RiedelF-macro: 0.1366 -> 0.2283Helped
use MIMLperf instead of MM-FβMM-Fβ · RiedelF-micro: 0.6559 -> 0.6686No clear effect
use MIMLperf instead of MM-Fβ, training timeMM-Fβ · Riedeltrain time: 4h 20m -> 1m 49sHelped
use MIMLperf instead of MM-FβMM-Fβ · Riedelrecall: 0.6521 -> 0.5781Hurt
train for F-micro instead of F-macro (ablation)MIMLperf · RiedelF-macro: 22.8354 -> 18.984Hurt
train for F-micro instead of F-macro (ablation)MIMLperf · RiedelF-micro: 61.0615 -> 66.8648Helped
use MIMLperf instead of MIMLSVM, training timeMIMLSVM · Scene lineartraining time: 7m 45s -> 1.66sHelped
use MIMLperf instead of MIMLSVM, test timeMIMLSVM · Scene lineartesting time per instance: 0.0920s -> 0.0009sHelped
use MIMLperf instead of MIMLSVM, training with RBFMIMLSVM · Scene RBFtraining time: 9m 24s -> 6m 40sHelped

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