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- W2000741950 abstract "Electric commerce of surpassing the traditional one appeared before the public and has currently led the change in the management of enterprises. To establish and maintain good relations with customers, electric commerce has various channels for customers that understand what they want to and suggest it to them. The bulletin board and e-mail among em are inbound information that enterprises can directly listen to customers` opinions and are different from other channels in characters. Enterprises can effectively manage the bulletin board and e-mail by understanding customers` ideas as many as possible and provide them with optimum answers. It is one of the important factors to improve the reliability of the notice board and e-mail as well as the whole electric commerce. Therefore this thesis researches into methods to classify various kinds of documents automatically in electric commerce; they are possible to solve existing problems of the bulletin board and e-mail, to operate effectively and to manage systematically. Moreover, it researches what the most suitable algorithm is in the automatic classification of Q&A documents by experiment the classifying performance of Naive Bayesian, TFIDF, Neural Network, k-NN" @default.
- W2000741950 created "2016-06-24" @default.
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- W2000741950 date "2006-02-01" @default.
- W2000741950 modified "2023-09-25" @default.
- W2000741950 title "Performance Evaluation on the Learning Algorithm for Automatic Classification of Q&A Documents" @default.
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- W2000741950 doi "https://doi.org/10.3745/kipstd.2006.13d.1.133" @default.
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