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Meta‐learning and the new challenges of machine learning

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Characterizing the applicability of classification algorithms using meta-level learning
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Automated Machine Learning
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Genetic Algorithm Essentials
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Dataset2Vec: learning dataset meta-features
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Big Data: A Survey
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Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction
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A survey of methods for distributed machine learning
inferredcites →https://doi.org/10.1016/b978-1-55860-335-6.50039-8openalex-snapshot
A Conservation Law for Generalization Performance
inferredcites →https://doi.org/10.1016/j.asoc.2004.12.002openalex-snapshot
On learning algorithm selection for classification
inferredcites →https://doi.org/10.1016/j.inffus.2017.02.004openalex-snapshot
Ensemble learning for data stream analysis: A survey
inferredcites →https://doi.org/10.1016/j.ins.2017.09.050openalex-snapshot
Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering
inferredcites →https://doi.org/10.1016/j.neucom.2013.05.048openalex-snapshot
MetaStream: A meta-learning based method for periodic algorithm selection in time-changing data
inferredcites →https://doi.org/10.1016/s0893-6080(05)80023-1openalex-snapshot
Stacked generalization
inferredcites →https://doi.org/10.1016/s1088-467x(99)00026-8openalex-snapshot
NOEMON: Design, implementation and performance results of an intelligent assistant for classifier selection
inferredcites →https://doi.org/10.1023/a:1007379606734openalex-snapshot
Multitask Learning
inferredcites →https://doi.org/10.1023/a:1021713901879openalex-snapshot
Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results
inferredcites →https://doi.org/10.1023/b:mach.0000015879.28004.9bopenalex-snapshot
A Meta-Learning Method to Select the Kernel Width in Support Vector Regression
inferredcites →https://doi.org/10.1080/08839519508945477openalex-snapshot
STATLOG: COMPARISON OF CLASSIFICATION ALGORITHMS ON LARGE REAL-WORLD PROBLEMS
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No free lunch theorems for optimization
inferredcites →https://doi.org/10.1109/access.2018.2886026openalex-snapshot
An Overview on Concept Drift Learning
inferredcites →https://doi.org/10.1109/mci.2009.4840311openalex-snapshot
IEEE Computational Intelligence Magazine
inferredcites →https://doi.org/10.1109/tkde.2011.58openalex-snapshot
DDD: A New Ensemble Approach for Dealing with Concept Drift
inferredcites →https://doi.org/10.1109/tkde.2018.2876857openalex-snapshot
Learning under Concept Drift: A Review
inferredcites →https://doi.org/10.1109/tnn.2011.2160459openalex-snapshot
Incremental Learning of Concept Drift in Nonstationary Environments
inferredcites →https://doi.org/10.1111/1742-6723.13145openalex-snapshot
Artificial intelligence and machine learning in emergency medicine
inferredcites →https://doi.org/10.1145/1456650.1456656openalex-snapshot
Cross-disciplinary perspectives on meta-learning for algorithm selection
inferredcites →https://doi.org/10.1145/2523813openalex-snapshot
A survey on concept drift adaptation
inferredcites →https://doi.org/10.1162/evco_a_00242openalex-snapshot
Automated Algorithm Selection: Survey and Perspectives
inferredcites →https://doi.org/10.1613/jair.1.11192openalex-snapshot
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
inferredcites →https://doi.org/10.25103/jestr.102.06openalex-snapshot
A Study of Algorithm Selection in Data Mining using Meta-Learning
inferredcites →https://doi.org/10.3390/ijerph17114179openalex-snapshot
Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1411.6767openalex-snapshot
Mobile Agent based Market Basket Analysis on Cloud
inferredcites →https://doi.org/10.5860/choice.194685openalex-snapshot
The master algorithm: how the quest for the ultimate learning machine will remake our world
inferredcites →https://doi.org/10.7551/mitpress/8291.001.0001openalex-snapshot
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