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Study on Text Categorization Method Based on Support Vector Machine

Author: YingWei
Tutor: WangZhengOu
School: Tianjin University
Course: Systems Engineering
Keywords: Text Mining Support Vector Machine Two types of classification Multi - class classification
CLC: TP391.1
Type: Master's thesis
Year: 2006
Downloads: 401
Quote: 2
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Data mining is an emerging technology , extracting useful information and knowledge from large-scale data and text mining is an important data mining . The face of large-scale , high -dimensional data , how to establish an effective text mining algorithm is one of the data mining research direction . Around the above problem , this paper conducted in-depth research on a number of issues involved in text classification data mining support vector machine mainly include the following aspects : the main reason for the slow speed of support vector training , the use of a pre- the FFMVM method to extract the boundary of the two types of samples relative boundary vector , fuzzy iteration algorithm to improve the speed of training support vector machine . On this basis , two types of text classification algorithm based on the improved support vector machine , a collection of pre - drawn boundary vector as the initial collection cycle iterative algorithm fuzzy support vector machine training , the experimental results show that the algorithm has higher efficiency compared with traditional methods . Proposed a new support vector machines multi- class classification method based on support vector machines multi- class classification methods exist for the current shortcomings , a SVM multi - class text classification algorithm . The experimental results show that this method is less than good performance DDAGSVM method , the number of support vector machine training , speed training , speed classification , while overcoming the regional presence of uncertain classification may .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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