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The Application of SVM to Decision Tree Induction

Author: CaoXueFeng
Tutor: WangXiZhao
School: Hebei University
Course: Applied Mathematics
Keywords: Support Vector Machine Inverse problem of support vector machine Interval Clustering Decision tree induction
CLC: TP18
Type: Master's thesis
Year: 2009
Downloads: 61
Quote: 1
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Abstract


The decision tree inductive learning algorithm is one of the most important machine learning algorithms . Typically using heuristic methods to build a decision tree , explore various decision tree heuristic algorithm became a focus of research . Support vector machine (SVM) is based on a small sample of the statistical learning theory learning method , taking into account the support vector machine classification interval generalization ability relationship , the SVM used in the decision tree induction process , using the maximum interval as generated decision tree heuristic information , decision tree generalization ability . This topic is based on the statistical learning theory and support vector machines , maximum interval as heuristic decision tree on small samples inductive learning research . In order to improve the generalization ability of the decision tree , in-depth analysis based on support vector machine and its inverse problem , the maximum interval support vector machine theory is applied to the decision tree induction process . Support vector machine and its fast algorithm , support vector machine is discussed on the basis of the inverse problem and its solution , given the use of k-means clustering algorithm solving the inverse problem of support vector machine . Then SVM decision tree induction , given the maximum interval as heuristic design algorithm generates decision , and the algorithm is analyzed . Finally, the experimental process , entropy for a binary decision tree heuristic comparison results show the effectiveness of the proposed algorithm .

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