Bayesian Least Squares Support Vector Machines for Classification of Ovarian Tumors
نویسندگان
چکیده
The aim of this study is to develop the Bayesian Least Squares Support Vector Machine (LS-SVM) classifiers, for preoperatively predicting the malignancy of ovarian tumors. We describe how to perform parameter estimation, input variable selection for LS-SVM within the evidence framework. The issue of computing the posterior class probability for risk minimization decision making is addressed. The relation between the LS-SVM model and kernel principal component analysis is also indicated and used for interpretation of the LS-SVM classifiers.
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