The Strength of Weak Learnability

Schapire, Robert E.·1990·Machine Learning

Cited by (1,455)

Items 601–700 of 1,455

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  4. Dynamic Classifier Selection for Effective Mining from Noisy Data Streams
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    Borut Sluban, Nada Lavrač A · 2015 · Neurocomputing
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    Ali Mohammed Alsaffar, Mostafa Nouri-Baygi, Hamed Majidi Zolbanin · 2024 · Journal Of Big Data
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    Vitaly Feldman, Parikshit Gopalan, Subhash Khot, Ashok Kumar Ponnuswami · 2009 · SIAM Journal on Computing
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    Shai Shalev‐Shwartz, Yoram Singer · 2010 · Machine Learning
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    M. Aaboud, G. Aad, B. Abbott, J. Abdallah, O. Abdinov, B. Abeloos, Rosemarie Zoë Aben, O. S. AbouZeid, N. L. Abraham, H. Abramowicz et al. · 2018 · Journal of High Energy Physics
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    Simon Günter, Horst Bunke A · 2004 · Pattern Recognition Letters
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  80. Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees
    Ming Zhang, John Fulcher · 1996 · IEEE Transactions on Neural Networks
  81. High-Dimensional Data Classification
    Vijay Pappu, Pãnos M. Pardalos · 2014 · Springer optimization and its applications
  82. Algorithm Selection on Data Streams
    Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren · 2014 · Lecture notes in computer science
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  98. Vote-boosting ensembles
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  100. Divide-and-conquer learning and modular perceptron networks
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