A new method for estimation of fetal weight using support vector machine

Chinese Journal of Biomedical Engineering

Volumn 23, Issue 6, 2004, Pages 516-522

  Song, Xiao Feng ^a   Han, Ping ^b   Zou, Li ^b   Chen, De Zhao ^c   Hu, Shang Xu ^c  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c ZHEJIANG UNIVERSITY   (China)

Author keywords

Fetal weight; Modeling; Regression estimation; Support vector machine

Indexed keywords

ALGORITHMS; PARAMETER ESTIMATION; REGRESSION ANALYSIS; TUNING;

FETAL WEIGHT; KERNEL FUNCTIONS; SUPPORT VECTOR MACHINES;

FETAL MONITORING;

ACCURACY; ALGORITHM; ARTICLE; AUTOMATION; CALCULATION; FETUS; FETUS WEIGHT; HUMAN; INTERMETHOD COMPARISON; KERNEL FUNCTION; MACHINE; MATHEMATICAL ANALYSIS; MODEL; PARAMETER; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE; TECHNIQUE;

EID: 11144238313     PISSN: 02588021     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. Vapnik VN. The Nature of Statistical Learning Theory[M]. Springer, New York, 1995.
2. Lin CJ. On the convergence of the decomposition method for support vector machines. IEEE Transactions on Neural Networks[J]. 2001, 12(6): 1288-1298.
3. Liao SP, Lin HT, Lin CJ. A Note on the Decomposition Methods for Support Vector Regression[J]. Neural Computation, 2002(14): 1267-1281.
4. Walczak B, Massart DL. The radial basis function - partial least squares approach as a flexible non - linear regression technique[J]. Analytica Chimica Acta, 1996, 331:177-185.