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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Machine learning, 2021-03, Vol.110 (3), p.457-506

2021

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Combining instance-based learning and logistic regression for multilabel classification

Machine learning, 2009-09, Vol.76 (2-3), p.211-225

2009

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How to measure uncertainty in uncertainty sampling for active learning

Machine learning, 2022, Vol.111 (1), p.89-122

2022

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Multilabel classification via calibrated label ranking

Machine learning, 2008-11, Vol.73 (2), p.133-153

2008

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On testing transitivity in online preference learning

Machine learning, 2021-08, Vol.110 (8), p.2063-2084

2021

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A flexible class of dependence-aware multi-label loss functions

Machine learning, 2022-02, Vol.111 (2), p.713-737

2022

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Learning monotone nonlinear models using the Choquet integral

Machine learning, 2012-10, Vol.89 (1-2), p.183-211

2012

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A critical analysis of variants of the AUC

Machine learning, 2008-09, Vol.72 (3), p.247-262

2008

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ML-Plan: Automated machine learning via hierarchical planning

Machine learning, 2018-09, Vol.107 (8-10), p.1495-1515

2018

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Multi-armed bandits with censored consumption of resources

Machine learning, 2023, Vol.112 (1), p.217-240

2023

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On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis

Machine learning, 2018-09, Vol.107 (8-10), p.1537-1560

2018

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Incremental permutation feature importance (iPFI): towards online explanations on data streams

Machine learning, 2023-12, Vol.112 (12), p.4863-4903

2023

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On label dependence and loss minimization in multi-label classification

Machine learning, 2012-07, Vol.88 (1-2), p.5-45

2012

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Algorithm selection on a meta level

Machine learning, 2023-04, Vol.112 (4), p.1253-1286

2023

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Introduction to the special issue of the ECML PKDD 2020 journal track

Machine learning, 2020-09, Vol.109 (9-10), p.1697-1698

2020

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Preference-based reinforcement learning: a formal framework and a policy iteration algorithm

Machine learning, 2012-10, Vol.89 (1-2), p.123-156

2012

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Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm

Machine learning, 2014-12, Vol.97 (3), p.327-351

2014

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Great Explanations: Opinionated Explanations for Recommendations

Case-Based Reasoning Research and Development, 2015, Vol.9343, p.244-258

2015

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Editorial: Preference learning and ranking

Machine learning, 2013-11, Vol.93 (2-3), p.185-189

2013

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CBR Model for Predicting a Building’s Electricity Use: On-Line Implementation in the Absence of Historical Data

Case-Based Reasoning Research and Development, 2015, p.306-319

2015

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Guest Editors’ introduction: special issue of the ECML/PKDD 2014 journal track

Machine learning, 2014-10, Vol.97 (1-2), p.1-3

2014

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Evidence-Driven Retrieval in Textual CBR: Bridging the Gap Between Retrieval and Reuse

Case-Based Reasoning Research and Development, 2015, Vol.9343, p.351-365

2015

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Mining Rank Data

Discovery Science, p.123-134

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Superset Learning Based on Generalized Loss Minimization

Machine Learning and Knowledge Discovery in Databases, p.260-275

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Addressing the Cold-Start Problem in Facial Expression Recognition

Case-Based Reasoning Research and Development, 2015, Vol.9343, p.197-211

2015

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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Combining instance-based learning and logistic regression for multilabel classification

How to measure uncertainty in uncertainty sampling for active learning

Multilabel classification via calibrated label ranking

On testing transitivity in online preference learning

A flexible class of dependence-aware multi-label loss functions

Learning monotone nonlinear models using the Choquet integral

A critical analysis of variants of the AUC

ML-Plan: Automated machine learning via hierarchical planning

Multi-armed bandits with censored consumption of resources

On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis

Incremental permutation feature importance (iPFI): towards online explanations on data streams

On label dependence and loss minimization in multi-label classification

Algorithm selection on a meta level

Introduction to the special issue of the ECML PKDD 2020 journal track

Preference-based reinforcement learning: a formal framework and a policy iteration algorithm

Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm

Great Explanations: Opinionated Explanations for Recommendations

Editorial: Preference learning and ranking

CBR Model for Predicting a Building’s Electricity Use: On-Line Implementation in the Absence of Historical Data

Guest Editors’ introduction: special issue of the ECML/PKDD 2014 journal track

Evidence-Driven Retrieval in Textual CBR: Bridging the Gap Between Retrieval and Reuse

Mining Rank Data

Superset Learning Based on Generalized Loss Minimization

Addressing the Cold-Start Problem in Facial Expression Recognition

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