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1
Machine learning from casual conversation
Machine learning from casual conversation
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Machine learning from casual conversation

Machine learning, 2023-12, Vol.112 (12), p.4789-4836 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-023-06383-0

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2
A survey on semi-supervised learning
A survey on semi-supervised learning
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A survey on semi-supervised learning

Machine learning, 2020-02, Vol.109 (2), p.373-440 [Peer Reviewed Journal]

The Author(s) 2019 ;Machine Learning is a copyright of Springer, (2019). All Rights Reserved. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-019-05855-6

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3
Learning from positive and unlabeled data: a survey
Learning from positive and unlabeled data: a survey
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Learning from positive and unlabeled data: a survey

Machine learning, 2020-04, Vol.109 (4), p.719-760 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020 ;The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-020-05877-5

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4
Unified SVM algorithm based on LS-DC loss
Unified SVM algorithm based on LS-DC loss
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Unified SVM algorithm based on LS-DC loss

Machine learning, 2023-08, Vol.112 (8), p.2975-3002 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2021. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05996-7

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5
Adaptive random forests for evolving data stream classification
Adaptive random forests for evolving data stream classification
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Adaptive random forests for evolving data stream classification

Machine learning, 2017-10, Vol.106 (9-10), p.1469-1495 [Peer Reviewed Journal]

The Author(s) 2017 ;Machine Learning is a copyright of Springer, 2017. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-017-5642-8

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6
Optimal classification trees
Optimal classification trees
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Optimal classification trees

Machine learning, 2017-07, Vol.106 (7), p.1039-1082 [Peer Reviewed Journal]

The Author(s) 2017 ;Machine Learning is a copyright of Springer, 2017. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-017-5633-9

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7
Exposing and explaining fake news on-the-fly
Exposing and explaining fake news on-the-fly
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Exposing and explaining fake news on-the-fly

Machine learning, 2024, Vol.113 (7), p.4615-4637 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-024-06527-w

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8
Kappa Updated Ensemble for drifting data stream mining
Kappa Updated Ensemble for drifting data stream mining
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Kappa Updated Ensemble for drifting data stream mining

Machine learning, 2020, Vol.109 (1), p.175-218 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 ;Machine Learning is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-019-05840-z

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9
HIVE-COTE 2.0: a new meta ensemble for time series classification
HIVE-COTE 2.0: a new meta ensemble for time series classification
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HIVE-COTE 2.0: a new meta ensemble for time series classification

Machine learning, 2021-12, Vol.110 (11-12), p.3211-3243 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-06057-9

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10
F: an interpretable transformation of the F-measure
F: an interpretable transformation of the F-measure
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F: an interpretable transformation of the F-measure

Machine learning, 2021-03, Vol.110 (3), p.451-456 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05964-1 ;PMID: 33746357

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11
autoBOT: evolving neuro-symbolic representations for explainable low resource text classification
autoBOT: evolving neuro-symbolic representations for explainable low resource text classification
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autoBOT: evolving neuro-symbolic representations for explainable low resource text classification

Machine learning, 2021, Vol.110 (5), p.989-1028 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05968-x ;PMID: 34720391

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12
Supersparse linear integer models for optimized medical scoring systems
Supersparse linear integer models for optimized medical scoring systems
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Supersparse linear integer models for optimized medical scoring systems

Machine learning, 2016-03, Vol.102 (3), p.349-391 [Peer Reviewed Journal]

The Author(s) 2015 ;The Author(s) 2016 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-015-5528-6

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13
Nearest neighbors distance ratio open-set classifier
Nearest neighbors distance ratio open-set classifier
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Nearest neighbors distance ratio open-set classifier

Machine learning, 2017-03, Vol.106 (3), p.359-386 [Peer Reviewed Journal]

The Author(s) 2016 ;Machine Learning is a copyright of Springer, 2017. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-016-5610-8

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14
Analysis of classifiers’ robustness to adversarial perturbations
Analysis of classifiers’ robustness to adversarial perturbations
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Analysis of classifiers’ robustness to adversarial perturbations

Machine learning, 2018-03, Vol.107 (3), p.481-508 [Peer Reviewed Journal]

The Author(s) 2017 ;Machine Learning is a copyright of Springer, (2017). All Rights Reserved. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-017-5663-3

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15
Data scarcity, robustness and extreme multi-label classification
Data scarcity, robustness and extreme multi-label classification
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Data scarcity, robustness and extreme multi-label classification

Machine learning, 2019-09, Vol.108 (8-9), p.1329-1351 [Peer Reviewed Journal]

The Author(s) 2019 ;Machine Learning is a copyright of Springer, (2019). All Rights Reserved. © 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-019-05791-5

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16
Classifier chains for multi-label classification
Classifier chains for multi-label classification
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Classifier chains for multi-label classification

Machine learning, 2011-12, Vol.85 (3), p.333-359 [Peer Reviewed Journal]

The Author(s) 2011 ;2015 INIST-CNRS ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-011-5256-5

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17
Bonsai: diverse and shallow trees for extreme multi-label classification
Bonsai: diverse and shallow trees for extreme multi-label classification
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Bonsai: diverse and shallow trees for extreme multi-label classification

Machine learning, 2020-11, Vol.109 (11), p.2099-2119 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-020-05888-2

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18
An instance level analysis of data complexity
An instance level analysis of data complexity
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An instance level analysis of data complexity

Machine learning, 2014-05, Vol.95 (2), p.225-256 [Peer Reviewed Journal]

The Author(s) 2013 ;2015 INIST-CNRS ;The Author(s) 2014 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-013-5422-z

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19
LoRAS: an oversampling approach for imbalanced datasets
LoRAS: an oversampling approach for imbalanced datasets
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LoRAS: an oversampling approach for imbalanced datasets

Machine learning, 2021-02, Vol.110 (2), p.279-301 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-020-05913-4

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20
Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing
Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing
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Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing

Machine learning, 2022-07, Vol.111 (7), p.2715-2740 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2021. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05972-1

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