The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
Support vector machine classification on the web
Bioinformatics
Gradient-Based Adaptation of General Gaussian Kernels
Neural Computation
Discrimination and calibration of mortality risk prediction models in interventional cardiology
Journal of Biomedical Informatics - Special issue: Clinical machine learning
2006 Special issue: Machine learning in soil classification
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Guest Editorial: Intelligent data analysis in biomedicine
Journal of Biomedical Informatics
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
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Support vector machines (SVM) have become popular among machine learning researchers, but their applications in biomedicine have been somewhat limited. A number of methods, such as grid search and evolutionary algorithms, have been utilized to optimize model parameters of SVMs. The sensitivity of the results to changes in optimization methods has not been investigated in the context of medical applications. In this study, radial-basis kernel SVM and polynomial kernel SVM mortality prediction models for percutaneous coronary interventions were optimized using (a) mean-squared error, (b) mean cross-entropy error, (c) the area under the receiver operating characteristic, and (d) the Hosmer-Lemeshow goodness-of-fit test (HL @g^2). A threefold cross-validation inner and outer loop method was used to select the best models using the training data, and evaluations were based on previously unseen test data. The results were compared to those produced by logistic regression models optimized using the same indices. The choice of optimization parameters had a significant impact on performance in both SVM kernel types.