SEARCH

SEARCH BY CITATION

References

  • 1
    De Smet F, De Brabanter J, Van den Bosch T, Pochet N, Amant F, Van Holsbeke C, Moerman P, De Moor B, Vergote I, Timmerman D. New models to predict depth of infiltration in endometrial carcinoma based on transvaginal sonography. Ultrasound Obstet Gynecol 2006; 27: 664671.
  • 2
    Suykens JAK, Van Gestel T, De Brabanter J, De Moor BLR, Vandewalle J. Least Squares Support Vector Machines. World Scientific: Singapore, 2002.
  • 3
    Epstein E, Skoog L, Isberg P, De Smet F, De Moor B, Olofsson P, Gudmundsson S, Valentin L. An algorithm including results of gray scale and power Doppler ultrasound examination to predict endometrial malignancy in women with postmenopausal bleeding. Ultrasound Obstet Gynecol 2002; 20: 370376.
  • 4
    Freedland SJ, Isaacs WB, Mangold LA, Yiu, SK, Grubb KA, Partin AW, Epstein JI, Walsh PC, Platz EA. Stronger association between obesity and biochemical progression after radical prostatectomy among men treated in the last 10 years. Clin Cancer Res 2005; 11: 28832888.
  • 5
    Timmerman D, Testa AC, Bourne T, Ferrazzi E, Ameye L, Konstantinovic ML, Van Calster B, Collins WP, Vergote I, Van Huffel S, Valentin L. Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group. J Clin Oncol 2005; 23: 87948801.
  • 6
    Vapnik VN. Statistical Learning Theory. John Wiley & Sons: New York, 1998.
  • 7
    Van Gestel T, Suykens JAK, Baesens B, Viaene S, Vanthienen J, Dedene G, De Moor BLR, Vandewalle J. Benchmarking least squares support vector machine classifiers. Machine Learning 2004; 54: 532.
  • 8