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A New Boosting Algorithm Using Input-Dependent Regularizer
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-46769-6_2openalex-snapshot
Theoretical Views of Boosting and Applications
inferredcites →https://doi.org/10.1007/978-1-4471-1599-1_26openalex-snapshot
An asymptotic analysis of AdaBoost in the binary classification case
inferredcites →https://doi.org/10.1007/978-1-4471-2099-5_30openalex-snapshot
The Effect of Adding Relevance Information in a Relevance Feedback Environment
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1023/a:1007607513941openalex-snapshot
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
inferredcites →https://doi.org/10.1023/a:1010852229904openalex-snapshot
An Adaptive Version of the Boost by Majority Algorithm
inferredcites →https://doi.org/10.1145/279943.279960openalex-snapshot
Improved boosting algorithms using confidence-rated predictions
inferredcites →https://doi.org/10.1162/neco.1994.6.2.181openalex-snapshot
Hierarchical Mixtures of Experts and the EM Algorithm
inferredcites →https://doi.org/10.1214/aos/1016218223openalex-snapshot
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
inferredcites →https://doi.org/10.1613/jair.614openalex-snapshot
Popular Ensemble Methods: An Empirical Study
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Boosting Algorithms: A Review of Methods, Theory, and Applications
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Generic Regularization of Boosting-Based Algorithms for the Discovery of Regime-Independent Portfolio Strategies from High-Noise Time Series
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