Quadratic Majorization for Nonconvex Loss with Applications to the Boosting Algorithm
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Publication:82722
DOI10.1080/10618600.2018.1424635OpenAlexW2782818778MaRDI QIDQ82722
Publication date: 6 June 2018
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10618600.2018.1424635
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- Greedy function approximation: A gradient boosting machine.
- The Adaptive Lasso and Its Oracle Properties
- Boosting algorithms: regularization, prediction and model fitting
- Monotonicity of quadratic-approximation algorithms
- The C-loss function for pattern classification
- Sharp quadratic majorization in one dimension
- A note on margin-based loss functions in classification
- Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
- On the optimization properties of the correntropic loss function in data analysis
- Robust penalized logistic regression with truncated loss functions
- Robust Truncated Hinge Loss Support Vector Machines
- Correntropy: Properties and Applications in Non-Gaussian Signal Processing
- Multicategory Support Vector Machines
- Variable Selection for Support Vector Machines in Moderately High Dimensions
- An adaptive version of the boost by majority algorithm