메뉴 건너뛰기




Volumn 1, Issue , 2013, Pages 401-404

A texture-based classification method for proteins in two-dimensional electrophoresis gel images: A feature selection method using Support Vector Machines and Genetic Algorithms

Author keywords

Electrophoresis; Feature selection; Genetic Algorithm; Support Vector Machines; Texture analysis

Indexed keywords

CLASSIFICATION METHODS; FEA-TURE SELECTIONS; FEATURE SELECTION METHODS; PROTEIN SEGMENTATION; TEXTURAL INFORMATION; TEXTURE ANALYSIS; TWO DIMENSIONAL ELECTROPHORESIS; TWO-DIMENSIONAL GEL ELECTROPHORESIS IMAGES;

EID: 84878232924     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (3)

References (14)
  • 1
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2): 121-167.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.C.1
  • 2
    • 0032594951 scopus 로고    scopus 로고
    • Support vector machines for histogram-based image classification
    • Chapelle, O., P. Haffner, et al. (1999). Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks 10(5): 1055-1064.
    • (1999) IEEE Transactions on Neural Networks , vol.10 , Issue.5 , pp. 1055-1064
    • Chapelle, O.1    Haffner, P.2
  • 6
    • 20844446602 scopus 로고    scopus 로고
    • Optimal replication and the importance of experimental design for gel-based quantitative proteomics
    • Hunt, S. M. N., M. R. Thomas, et al. (2005). Optimal Replication and the Importance of Experimental Design for Gel-Based Quantitative Proteomics. Journal of Proteome Research 4(3): 809-819.
    • (2005) Journal of Proteome Research , vol.4 , Issue.3 , pp. 809-819
    • Hunt, S.M.N.1    Thomas, M.R.2
  • 7
    • 0347721633 scopus 로고    scopus 로고
    • A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers
    • Kudo, M. and J. Sklansky (1998). A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers. Kybernetika 34(4): 429-434.
    • (1998) Kybernetika , vol.34 , Issue.4 , pp. 429-434
    • Kudo, M.1    Sklansky, J.2
  • 8
    • 2542625918 scopus 로고    scopus 로고
    • Support vector machines for transient stability analysis of large-scale power systems
    • Moulin, L. S., A. P. Alves Da Silva, et al. (2004). Support vector machines for transient stability analysis of large-scale power systems. IEEE Transactions on Power Systems 19(2): 818-825.
    • (2004) IEEE Transactions on Power Systems , vol.19 , Issue.2 , pp. 818-825
    • Moulin, L.S.1    Alves Da Silva, A.P.2
  • 9
    • 54249129943 scopus 로고    scopus 로고
    • An evolutionary approach for learning motion class patterns
    • Müller, M., B. Demuth, et al. (2008). An evolutionary approach for learning motion class patterns. 5096 LNCS: 365-374.
    • (2008) LNCS , vol.5096 , pp. 365-374
    • Müller, M.1    Demuth, B.2
  • 10
    • 77957220211 scopus 로고    scopus 로고
    • Two-dimensional gel electrophoresis in proteomics: Past, present and future
    • Rabilloud, T., M. Chevallet, et al. (2010). Two-dimensional gel electrophoresis in proteomics: Past, present and future. Journal of Proteomics 73(11): 2064-2077.
    • (2010) Journal of Proteomics , vol.73 , Issue.11 , pp. 2064-2077
    • Rabilloud, T.1    Chevallet, M.2
  • 11
    • 0024895461 scopus 로고
    • A note on genetic algorithms for large-scale feature selection
    • Siedlecki, W. and J. Sklansky (1989). A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters 10(5): 335-347.
    • (1989) Pattern Recognition Letters , vol.10 , Issue.5 , pp. 335-347
    • Siedlecki, W.1    Sklansky, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.