Sparse Logistic Regression with the Hybrid L1/2+1 Regularization
Author:
Affiliation:
1. University of Electronic Science and Technology of China, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3460569.3460578
Reference17 articles.
1. Thompson E Bowling B and Markle R. 2018. Predicting Student Success in a Major's Introductory Biology Course via Logistic Regression Analysis of Scientific Reasoning Ability and Mathematics Scores. J. Research in Science Education. 48 (February 2018): 151-163. https://doi.org/10.1007/s11165-016-9563-5 Thompson E Bowling B and Markle R. 2018. Predicting Student Success in a Major's Introductory Biology Course via Logistic Regression Analysis of Scientific Reasoning Ability and Mathematics Scores. J. Research in Science Education. 48 (February 2018): 151-163. https://doi.org/10.1007/s11165-016-9563-5
2. Risk of attrition in a longitudinal study of skin cancer: logistic and survival models can give different results
3. Gajović V Kerkez M and Kočović J. 2018. Modeling and simulation of logistic processes: risk assessment with a fuzzy logic technique. J. Simulation (San Diego Calif.). 94 (June 2018): 507-518. https://doi.org/10.1177/0037549717738351 Gajović V Kerkez M and Kočović J. 2018. Modeling and simulation of logistic processes: risk assessment with a fuzzy logic technique. J. Simulation (San Diego Calif.). 94 (June 2018): 507-518. https://doi.org/10.1177/0037549717738351
4. Ridge Regression: Biased Estimation for Nonorthogonal Problems
5. Regression Shrinkage and Selection Via the Lasso. J;Tibshirani R.;Journal of the Royal Statistical Society: Series B. 58,1996
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