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1
InceptionTime: Finding AlexNet for time series classification
InceptionTime: Finding AlexNet for time series classification
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InceptionTime: Finding AlexNet for time series classification

Data mining and knowledge discovery, 2020-11, Vol.34 (6), p.1936-1962 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-020-00710-y

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2
ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
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ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels

Data mining and knowledge discovery, 2020-09, Vol.34 (5), p.1454-1495 [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: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-020-00701-z

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3
Deep learning for time series classification: a review
Deep learning for time series classification: a review
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Deep learning for time series classification: a review

Data mining and knowledge discovery, 2019-07, Vol.33 (4), p.917-963 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 ;Data Mining and Knowledge Discovery is a copyright of Springer, (2019). All Rights Reserved. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-019-00619-1

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4
catch22: CAnonical Time-series CHaracteristics: Selected through highly comparative time-series analysis
catch22: CAnonical Time-series CHaracteristics: Selected through highly comparative time-series analysis
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catch22: CAnonical Time-series CHaracteristics: Selected through highly comparative time-series analysis

Data mining and knowledge discovery, 2019-11, Vol.33 (6), p.1821-1852 [Peer Reviewed Journal]

The Author(s) 2019 ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-019-00647-x

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5
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
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The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Data mining and knowledge discovery, 2017-05, Vol.31 (3), p.606-660 [Peer Reviewed Journal]

The Author(s) 2016 ;Data Mining and Knowledge Discovery is a copyright of Springer, 2017. ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-016-0483-9 ;PMID: 30930678

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6
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
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On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study

Data mining and knowledge discovery, 2016-07, Vol.30 (4), p.891-927 [Peer Reviewed Journal]

The Author(s) 2016 ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-015-0444-8

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7
Characterizing concept drift
Characterizing concept drift
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Characterizing concept drift

Data mining and knowledge discovery, 2016-07, Vol.30 (4), p.964-994 [Peer Reviewed Journal]

The Author(s) 2016 ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-015-0448-4

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8
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
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The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Data mining and knowledge discovery, 2021-03, Vol.35 (2), p.401-449 [Peer Reviewed Journal]

The Author(s) 2020 ;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: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-020-00727-3 ;PMID: 33679210

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9
MultiRocket: multiple pooling operators and transformations for fast and effective time series classification
MultiRocket: multiple pooling operators and transformations for fast and effective time series classification
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MultiRocket: multiple pooling operators and transformations for fast and effective time series classification

Data mining and knowledge discovery, 2022-09, Vol.36 (5), p.1623-1646 [Peer Reviewed Journal]

Crown 2022 ;Crown 2022. 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: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-022-00844-1

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10
TS-CHIEF: a scalable and accurate forest algorithm for time series classification
TS-CHIEF: a scalable and accurate forest algorithm for time series classification
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TS-CHIEF: a scalable and accurate forest algorithm for time series classification

Data mining and knowledge discovery, 2020-05, Vol.34 (3), p.742-775 [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. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-020-00679-8

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11
Graph based anomaly detection and description: a survey
Graph based anomaly detection and description: a survey
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Graph based anomaly detection and description: a survey

Data mining and knowledge discovery, 2015-05, Vol.29 (3), p.626-688 [Peer Reviewed Journal]

The Author(s) 2014 ;The Author(s) 2015 ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-014-0365-y

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12
The BOSS is concerned with time series classification in the presence of noise
The BOSS is concerned with time series classification in the presence of noise
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The BOSS is concerned with time series classification in the presence of noise

Data mining and knowledge discovery, 2015-11, Vol.29 (6), p.1505-1530 [Peer Reviewed Journal]

The Author(s) 2014 ;The Author(s) 2015 ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-014-0377-7

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13
A review on distance based time series classification
A review on distance based time series classification
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A review on distance based time series classification

Data mining and knowledge discovery, 2019-03, Vol.33 (2), p.378-412 [Peer Reviewed Journal]

The Author(s) 2018 ;Data Mining and Knowledge Discovery is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-018-0596-4

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14
Experimental comparison of representation methods and distance measures for time series data
Experimental comparison of representation methods and distance measures for time series data
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Experimental comparison of representation methods and distance measures for time series data

Data mining and knowledge discovery, 2013-03, Vol.26 (2), p.275-309 [Peer Reviewed Journal]

The Author(s) 2012 ;The Author(s) 2013 ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-012-0250-5

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15
Algorithmic fairness datasets: the story so far
Algorithmic fairness datasets: the story so far
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Algorithmic fairness datasets: the story so far

Data mining and knowledge discovery, 2022-11, Vol.36 (6), p.2074-2152 [Peer Reviewed Journal]

The Author(s) 2022 ;The Author(s) 2022. 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: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-022-00854-z

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16
Measuring discrimination in algorithmic decision making
Measuring discrimination in algorithmic decision making
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Measuring discrimination in algorithmic decision making

Data mining and knowledge discovery, 2017-07, Vol.31 (4), p.1060-1089 [Peer Reviewed Journal]

The Author(s) 2017 ;Data Mining and Knowledge Discovery is a copyright of Springer, 2017. ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-017-0506-1

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17
Training and assessing classification rules with imbalanced data
Training and assessing classification rules with imbalanced data
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Training and assessing classification rules with imbalanced data

Data mining and knowledge discovery, 2014, Vol.28 (1), p.92-122 [Peer Reviewed Journal]

The Author(s) 2012 ;The Author(s) 2014 ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-012-0295-5

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18
Classification of time series by shapelet transformation
Classification of time series by shapelet transformation
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Classification of time series by shapelet transformation

Data mining and knowledge discovery, 2014-07, Vol.28 (4), p.851-881 [Peer Reviewed Journal]

The Author(s) 2013 ;The Author(s) 2014 ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-013-0322-1

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19
A survey of community detection methods in multilayer networks
A survey of community detection methods in multilayer networks
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A survey of community detection methods in multilayer networks

Data mining and knowledge discovery, 2021-01, Vol.35 (1), p.1-45 [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: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-020-00716-6

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20
Proximity Forest: an effective and scalable distance-based classifier for time series
Proximity Forest: an effective and scalable distance-based classifier for time series
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Proximity Forest: an effective and scalable distance-based classifier for time series

Data mining and knowledge discovery, 2019-05, Vol.33 (3), p.607-635 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 ;Data Mining and Knowledge Discovery is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-019-00617-3

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Results 1 - 20 of 846  for All Library Resources

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