메뉴 건너뛰기




Volumn 44, Issue 1, 2011, Pages 97-106

Sparse ensembles using weighted combination methods based on linear programming

Author keywords

Classifier ensemble; k nearest neighbor; Linear programming; Linear weighted combination; Sparse ensembles

Indexed keywords

CLASSIFIER ENSEMBLES; COMBINATION METHOD; DATA SETS; ENSEMBLE CLASSIFIERS; ENSEMBLE LEARNING; HIGH RESOLUTION RANGE PROFILES; K-NEAREST NEIGHBORS; LINEAR PROGRAMMING PROBLEM; LINEAR WEIGHTED COMBINATION; MACHINE-LEARNING; MULTIPLE CLASSIFIERS; SPARSE ENSEMBLES; STRUCTURE RISK MINIMIZATION; TRAINING ERRORS; WEIGHT COEFFICIENTS; WEIGHT VECTOR;

EID: 77956456333     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2010.07.021     Document Type: Article
Times cited : (117)

References (48)
  • 2
    • 0007729457 scopus 로고    scopus 로고
    • Multiple Classifier Systems
    • T. Windeatt, F. Roli (Eds.) Springer
    • T. Windeatt, F. Roli (Eds.), Multiple Classifier Systems, in: Lecture Notes in Computer Science, vol. 2709, Springer, 2003.
    • (2003) Lecture Notes in Computer Science , vol.2709
  • 3
    • 0003033811 scopus 로고    scopus 로고
    • Multiple Classifier Systems
    • F. Roli, J. Kittler, T. Windeatt (Eds.) Springer
    • F. Roli, J. Kittler, T. Windeatt (Eds.), Multiple Classifier Systems, in: Lecture Notes in Computer Science, vol. 3077, Springer, 2004.
    • (2004) Lecture Notes in Computer Science , vol.3077
  • 5
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman Bagging predictors Machine Learning 24 1996 123 140
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 12
    • 0033893813 scopus 로고    scopus 로고
    • Optimal linear combination of neural networks for improving classification performance
    • N. Ueda Optimal linear combination of neural networks for improving classification performance IEEE Transactions on Pattern Analysis and Machine Intelligence 22 2 2000 207 215
    • (2000) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.22 , Issue.2 , pp. 207-215
    • Ueda, N.1
  • 15
    • 0030196364 scopus 로고    scopus 로고
    • Stacked regressions
    • L. Breiman Stacked regressions Machine Learning 24 1996 49 64
    • (1996) Machine Learning , vol.24 , pp. 49-64
    • Breiman, L.1
  • 16
    • 21244501361 scopus 로고    scopus 로고
    • A theoretical and experimental analysis of linear combiners for multiple classifier systems
    • G. Fumera, and F. Roli A theoretical and experimental analysis of linear combiners for multiple classifier systems IEEE Transactions on Pattern Analysis and Machine Intelligence 27 6 2005 942 956
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , Issue.6 , pp. 942-956
    • Fumera, G.1    Roli, F.2
  • 20
    • 0029521676 scopus 로고
    • Sample compression learnability, and the VapnikChervonenkis dimension
    • S. Floyd, and M. Marmuth Sample compression learnability, and the VapnikChervonenkis dimension Machine Learning 21 3 1995 269 304
    • (1995) Machine Learning , vol.21 , Issue.3 , pp. 269-304
    • Floyd, S.1    Marmuth, M.2
  • 21
    • 76849107689 scopus 로고    scopus 로고
    • On the sparseness of 1-norm support vector machines
    • L. Zhang, and W.D. Zhou On the sparseness of 1-norm support vector machines Neural Networks 23 3 2010 373 385
    • (2010) Neural Networks , vol.23 , Issue.3 , pp. 373-385
    • Zhang, L.1    Zhou, W.D.2
  • 23
    • 0036567392 scopus 로고    scopus 로고
    • Ensembling neural networks: Many could be better than all
    • Z.H. Zhou, J.X. Wu, and W. Tang Ensembling neural networks: many could be better than all Artificial Intelligence 137 12 2002 239 263
    • (2002) Artificial Intelligence , vol.137 , Issue.12 , pp. 239-263
    • Zhou, Z.H.1    Wu, J.X.2    Tang, W.3
  • 28
    • 33750460241 scopus 로고    scopus 로고
    • Using boosting to prune bagging ensembles
    • G. Martnez-Muoz, and A. Surez Using boosting to prune bagging ensembles Pattern Recognition Letters 28 1 2007 156 165
    • (2007) Pattern Recognition Letters , vol.28 , Issue.1 , pp. 156-165
    • Martnez-Muoz, G.1    Surez, A.2
  • 34
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Mining and Knowledge Discovery 2 2 1998 121 167
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.C.1
  • 35
    • 0001219859 scopus 로고
    • Regularization theory and neural networks architectures
    • F. Girosi, M. Jones, and T. Poggio Regularization theory and neural networks architectures Neural Computation 7 2 1995 219 269
    • (1995) Neural Computation , vol.7 , Issue.2 , pp. 219-269
    • Girosi, F.1    Jones, M.2    Poggio, T.3
  • 36
    • 0000249788 scopus 로고    scopus 로고
    • An equivalence between sparse approximation and support vector machines
    • F. Girosi An equivalence between sparse approximation and support vector machines Neural Computation 10 6 1998 1455 1480
    • (1998) Neural Computation , vol.10 , Issue.6 , pp. 1455-1480
    • Girosi, F.1
  • 37
    • 33745801137 scopus 로고    scopus 로고
    • Exact 1-norm support vector machines via unconstrained convex differentiable minimization
    • O. Mangasarian Exact 1-norm support vector machines via unconstrained convex differentiable minimization Journal of Machine Learning Research 7 2006 1517 1530
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1517-1530
    • Mangasarian, O.1
  • 41
    • 3543109140 scopus 로고    scopus 로고
    • A feature selection Newton method for support vector machine classification
    • G.M. Fung, and O.L. Mangasarian A feature selection Newton method for support vector machine classification Computational Optimization and Applications 28 2004 185 202
    • (2004) Computational Optimization and Applications , vol.28 , pp. 185-202
    • Fung, G.M.1    Mangasarian, O.L.2
  • 42
    • 0036887673 scopus 로고    scopus 로고
    • Linear programming support vector machines
    • W.D. Zhou, L. Zhang, and L.C. Jiao Linear programming support vector machines Pattern Recognition 35 12 2002 2927 2936
    • (2002) Pattern Recognition , vol.35 , Issue.12 , pp. 2927-2936
    • Zhou, W.D.1    Zhang, L.2    Jiao, L.C.3
  • 45
    • 33744538338 scopus 로고    scopus 로고
    • A two-distribution compounded statistical model for radar HRRP target recognition
    • L. Du, H.W. Liu, Z. Bao, and J.Y. Zhang A two-distribution compounded statistical model for radar HRRP target recognition IEEE Transactions on Signal Processing 54 6 2006 2226 2238
    • (2006) IEEE Transactions on Signal Processing , vol.54 , Issue.6 , pp. 2226-2238
    • Du, L.1    Liu, H.W.2    Bao, Z.3    Zhang, J.Y.4
  • 46
    • 38849204864 scopus 로고    scopus 로고
    • Optimizing the data-dependent kernel under a unified kernel optimization framework
    • B. Chen, H. Liu, and Z. Bao Optimizing the data-dependent kernel under a unified kernel optimization framework Pattern Recognition 41 6 2008 2107 2119
    • (2008) Pattern Recognition , vol.41 , Issue.6 , pp. 2107-2119
    • Chen, B.1    Liu, H.2    Bao, Z.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.