Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines

By Chuan Lu, Tony Van Gestel, Johan A. K. Suykens, S. Van Huffel, I. Vergote, D. Timmerman. Artif. Intell. Medicine, 2003

319 citations recorded by the source corpus.

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Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes (2004, unknown): “SVM have already been used in various ﬁelds such as diagnosis ovarian tumor malignancy prediction [10], image classiﬁcation [11] [12] and spam categorization [13].”
Sample size estimation in diagnostic test studies of biomedical informatics (2014, unknown): “In another study, several least square vector machines for prediction of preoperative malignancy of ovarian tumors have been developed and assessed [11].”
Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation (2012, methodology): “Examples include linear regression (Wu, 1986), splines (Wahba, 1980), support vector regression (Cawley and Talbot, 2004), and Kriging models (Sundararajan and Keerthi, 2001).”
A Least-squares Approach to Direct Importance Estimation (2009, methodology): “Theℓ2-r gularization method will be used for theoretical analysis in Section 3.3.”
Regularized Max Pooling for Image Categorization (2014, background): “A more gentle derivation of the above formula is given in [2].”