Article

Effective Learning in Dynamic Environments by Explicit Context Tracking

Authors:

Gerhard Widmer,

Miroslav KubatAuthors Info & Claims

ECML '93: Proceedings of the European Conference on Machine Learning

Pages 227 - 243

Published: 05 April 1993 Publication History

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Choudhary AJha PTiwari ABharill NRatnaparkhe M(2021)Scalable Fuzzy Clustering-based Regression to Predict the Isoelectric Points of the Plant Protein Sequences using Apache Spark2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ45933.2021.9494447(1-6)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1109/FUZZ45933.2021.9494447
Sun JFujita HChen PLi H(2017)Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensembleKnowledge-Based Systems10.1016/j.knosys.2016.12.019120:C(4-14)Online publication date: 15-Mar-2017
https://dl.acm.org/doi/10.1016/j.knosys.2016.12.019
Shaker AHüllermeier E(2015)Recovery analysis for adaptive learning from non-stationary data streamsNeurocomputing10.1016/j.neucom.2014.09.076150:PA(250-264)Online publication date: 20-Feb-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.09.076
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Published In

ECML '93: Proceedings of the European Conference on Machine Learning

April 1993

461 pages

ISBN:3540566023

Editor:
Pavel Brazdil

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 05 April 1993

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Choudhary AJha PTiwari ABharill NRatnaparkhe M(2021)Scalable Fuzzy Clustering-based Regression to Predict the Isoelectric Points of the Plant Protein Sequences using Apache Spark2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ45933.2021.9494447(1-6)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1109/FUZZ45933.2021.9494447
Sun JFujita HChen PLi H(2017)Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensembleKnowledge-Based Systems10.1016/j.knosys.2016.12.019120:C(4-14)Online publication date: 15-Mar-2017
https://dl.acm.org/doi/10.1016/j.knosys.2016.12.019
Shaker AHüllermeier E(2015)Recovery analysis for adaptive learning from non-stationary data streamsNeurocomputing10.1016/j.neucom.2014.09.076150:PA(250-264)Online publication date: 20-Feb-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.09.076
Gama JŽliobaitė IBifet APechenizkiy MBouchachia A(2014)A survey on concept drift adaptationACM Computing Surveys10.1145/252381346:4(1-37)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1145/2523813
Bartocha KPodolak I(2011)Classifier ensembles for virtual concept drift - the DEnBoost algorithmProceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II10.5555/2021503.2021527(164-171)Online publication date: 23-May-2011
https://dl.acm.org/doi/10.5555/2021503.2021527
Wang SSchlobach SKlein M(2010)What is concept drift and how to measure it?Proceedings of the 17th international conference on Knowledge engineering and management by the masses10.5555/1948294.1948318(241-256)Online publication date: 11-Oct-2010
https://dl.acm.org/doi/10.5555/1948294.1948318
Ang HGopalkrishnan VNg WHoi S(2010)On classifying drifting concepts in P2P networksProceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I10.5555/1888258.1888268(24-39)Online publication date: 20-Sep-2010
https://dl.acm.org/doi/10.5555/1888258.1888268
Ang HGopalkrishnan VNg WHoi S(2010)On classifying drifting concepts in P2P networksProceedings of the 2010th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I10.1007/978-3-642-15880-3_8(24-39)Online publication date: 20-Sep-2010
https://dl.acm.org/doi/10.1007/978-3-642-15880-3_8
Nehme RRundensteiner EBertino E(2009)Self-tuning query mesh for adaptive multi-route query processingProceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology10.1145/1516360.1516452(803-814)Online publication date: 24-Mar-2009
https://dl.acm.org/doi/10.1145/1516360.1516452
Beringer JHüllermeier E(2007)An efficient algorithm for instance-based learning on data streamsProceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications10.5555/1770770.1770776(34-48)Online publication date: 14-Jul-2007
https://dl.acm.org/doi/10.5555/1770770.1770776
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Effective learning in dynamic environments by explicit context tracking

Tracking Context Changes through Meta-Learning

Robust object tracking in crowd dynamic scenes using explicit stereo depth

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