Support vector machines (SVMs) are a powerful tool for classification problems. SVMs have only been developed in the last 20 years with the availability of cheap and abundant computing power. SVMs are a non-statistical approach and make no assumptions about the distribution of the data. Here support vector machines are applied to a classic data set from the machine learning literature and the out-of-sample misclassification rates are compared to other classification methods. Finally, an algorithm for using support vector machines to address the difficulty in imputing missing categorical data is proposed and its performance is demonstrated under three different scenarios using data from the 1997 National Labor Survey.
College and Department
Physical and Mathematical Sciences; Statistics
BYU ScholarsArchive Citation
Rogers, Spencer David, "Support Vector Machines for Classification and Imputation" (2012). All Theses and Dissertations. 3215.
support vector machines, SVM, imputation, binary classification, handwritten digit recognition, EM algorithm, NLSY97