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Volumn 22, Issue , 2012, Pages 246-254

Fast, exact model selection and permutation testing for ℓ2-regularized logistic regression

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ITERATIVE METHODS; LEARNING SYSTEMS;

EID: 84896548020     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (30)

References (20)
  • 4
    • 77950537175 scopus 로고    scopus 로고
    • Reg-ularization paths for generalized linear models via coordinate descent
    • J. Friedman, T. Hastie and R. Tibshirani (2010). Reg-ularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 33(1):1-22.
    • (2010) Journal of Statistical Software , vol.33 , Issue.1 , pp. 1-22
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 5
    • 34548105186 scopus 로고    scopus 로고
    • Large-scale Bayesian logistic regression for text categorization
    • A. Genkin, D.D. Lewis and D. Madigan (2007). Large-Scale Bayesian Logistic Regression for Text Categorization. Technometrics 49(3): 291-304.
    • (2007) Technometrics , vol.49 , Issue.3 , pp. 291-304
    • Genkin, A.1    Lewis, D.D.2    Madigan, D.3
  • 6
    • 67349153307 scopus 로고    scopus 로고
    • Single-trial discrimination for integrating simultaneous EEG and fMRI: Identifying cortical areas contributing to trial-to-trial variability in the auditory oddball task
    • R.I. Goldman, C.Y. Wei, M.G. Philiastides, A.D. Ger-son, D. Friedman, T.R. Brown and P. Sajda (2009). Single-trial discrimination for integrating simultaneous EEG and fMRI: Identifying cortical areas contributing to trial-to-trial variability in the auditory oddball task. NeuroImage 47(1) 136-147.
    • (2009) NeuroImage , vol.47 , Issue.1 , pp. 136-147
    • Goldman, R.I.1    Wei, C.Y.2    Philiastides, M.G.3    Ger-Son, A.D.4    Friedman, D.5    Brown, T.R.6    Sajda, P.7
  • 12
    • 33845197904 scopus 로고    scopus 로고
    • Making logistic regression a core data mining tool: A practical investigation of accuracy, speed, and simplicity
    • Robotics Institute, Carnegie Mellon University
    • P. Komarek and A. Moore (2005). Making Logistic Regression A Core Data Mining Tool: A Practical Investigation of Accuracy, Speed, and Simplicity. CMU Tech. Report CMU-RI-TR-05-27, Robotics Institute, Carnegie Mellon University.
    • (2005) CMU Tech. Report CMU-RI-TR-05-27
    • Komarek, P.1    Moore, A.2
  • 15
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Y. LeCun, L. Bottou, Y. Bengio and P. Haffner (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278-2324.
    • (1998) Proceedings of the IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 16
    • 77954676863 scopus 로고    scopus 로고
    • Permutation tests for studying classifier performance
    • M. Ojala and G. C. Garriga (2010). Permutation Tests for Studying Classifier Performance. Journal of Machine Learning Research 11:1833-1863.
    • (2010) Journal of Machine Learning Research , vol.11 , pp. 1833-1863
    • Ojala, M.1    Garriga, G.C.2
  • 19
    • 0001868572 scopus 로고    scopus 로고
    • Text categorization based on regularized linear classifiers
    • T. Zhang and F. Oles (2001). Text Categorization Based on Regularized Linear Classifiers. Information Retrieval 4:5-31.
    • (2001) Information Retrieval , vol.4 , pp. 5-31
    • Zhang, T.1    Oles, F.2
  • 20
    • 0042879446 scopus 로고    scopus 로고
    • Leave-one-out bounds for kernel methods
    • T. Zhang (2003). Leave-One-Out Bounds for Kernel Methods. Neural Computation 15(6):1397-1437.
    • (2003) Neural Computation , vol.15 , Issue.6 , pp. 1397-1437
    • Zhang, T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.