| Summary: | 碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 98 === Support Vector Machines (SVM) has been the most commonly used classification method in recent years. Its main theory originated from Structural Risk Minimization (SRM), a new-generation learning algorithm based on statistical learning theories. These algorithms are currently applied in various fields, including bioinformatics, image analysis, handwriting recognition, daily life anomaly analysis, credit card fraud, and surveillance video detection. Classification through SVM is more accurate and more stable than maximum likelihood estimation (MLE), and does not have frequent inconsistencies like MLE. SVM is also more effective in terms of image segmentation. Thus, this study used DM to incorporate SVM for classification, and used Bayesian Networks (BN) and Decision Trees (DT) to analyze 4 UCI (University of California – Irvine) databases and compared the results with past studies. Results showed that the integration of SVM and DT improved the accuracy rate of classification. Thus, the use of this method to establish a classification system is valid.
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