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Full-Text Articles in Physical Sciences and Mathematics
Support Vector Machines For Classification And Imputation, Spencer David Rogers
Support Vector Machines For Classification And Imputation, Spencer David Rogers
Theses and Dissertations
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 …
Computation Of Weights For Probabilistic Record Linkage Using The Em Algorithm, G. John Bauman
Computation Of Weights For Probabilistic Record Linkage Using The Em Algorithm, G. John Bauman
Theses and Dissertations
Record linkage is the process of combining information about a single individual from two or more records. Probabilistic record linkage gives weights to each field that is compared. The decision of whether the records should be linked is then determined by the sum of the weights, or “Score”, over all fields compared. Using methods similar to the simple versus simple most powerful test, an optimal record linkage decision rule can be established to minimize the number of unlinked records when the probability of false positive and false negative errors are specified. The weights needed for probabilistic record linkage necessitate linking …