ParaFIS adds a drift anticipation module to evolving fuzzy systems to speed adaptation after abrupt concept drifts, showing improved reactivity and final accuracy on UCI datasets with artificial drifts.
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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
Bootstrap and Bayesian uncertainty estimates for ordinal embeddings from triplet data are shown to be well-calibrated in simulations.
citing papers explorer
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ParaFIS:A new online fuzzy inference system based on parallel drift anticipation
ParaFIS adds a drift anticipation module to evolving fuzzy systems to speed adaptation after abrupt concept drifts, showing improved reactivity and final accuracy on UCI datasets with artificial drifts.
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Uncertainty Estimates for Ordinal Embeddings
Bootstrap and Bayesian uncertainty estimates for ordinal embeddings from triplet data are shown to be well-calibrated in simulations.