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The Classification Technology Research of Data Mining Based on SVM

Author: ZhaoHui
Tutor: LiuZhiJing
School: Xi'an University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: Data Mining Classification Support Vector Machine Kernel function
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 245
Quote: 2
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With the increasing development of computer technology and network communication technology , the front of people flock to the large amounts of data . How to effectively select the information has become an increasingly prominent problem , data mining technology is responsive to this need and developed . Classification techniques as data mining is an important aspect of the technology , has always been the concern of researchers have produced a lot of good solution , this paper studies Support Vector Machine method is a very effective classification method , which is based on statistics learning theory and developed a new classification method . The use of structural risk minimization principle instead of empirical risk minimization principle, be used to deal with the case of small sample learning problem . The kernel function thinking nonlinear problem can be transformed into a linear problem to solve , and reduce the complexity of the algorithm . This paper first discusses some basic knowledge of statistical learning theory , including machine learning , VC dimension , the promotion of the profession and the structural risk minimization , etc. . Next focuses on support vector machines , including the history and current status of the development , the main basic concepts and contents of multiple classification process for several lack of multi- class classification algorithm based on support vector machine analysis , the use of the new multi - value classification algorithm based on the relative degrees of separation , the final numerical experiments show that the algorithm is feasible and effective . Another disadvantage of slow speed of training support vector machines , the analysis of the nature and process of incremental learning support vector given a new incremental learning algorithm , the subsequent numerical experiments also show that the method can ensure the accuracy of the test at the same time reduce the training time . Domestic support vector machine is only limited to the theoretical aspects of the application has only just begun . Is based on the application of technology , so a lot of knowledge and experience we need in practice further summarize and found the support vector machine .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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