Support Vector Machines for Imperfect Nonlinear Data (200 Patients with Sepsis)
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Support vector machines is a simplified cluster program that does not apply all observations but rather the difficult observations lying close to the separation lines. The basic aim of support vector machines is to construct the best fit separation line (or with three dimensional data separation plane), separating cases and controls as good as possible. Discriminant analysis (Chap. 22), classification trees or decision trees (Chap. 8), and neural networks (Chap. 55), are alternative methods for the purpose, but support vector machines are generally more stable and sensitive, although heuristic studies to indicate when they perform better are missing. Support vector machines are also often used in automatic modeling that computes the ensembled results of several best fit models see the Chaps. 69 and 70).