The 595th session of the Xiangshan Science Conferences was recently held in Fragrant Hill Hotel,Beijing,on May 22-23,2017.The theme of this highly successful session was on＂Health Risk ad Control of Disinfection By-p...The 595th session of the Xiangshan Science Conferences was recently held in Fragrant Hill Hotel,Beijing,on May 22-23,2017.The theme of this highly successful session was on＂Health Risk ad Control of Disinfection By-products（DBPs）in China＂.More than fifty prominent scientists from Australia,Canada,China,and the United States actively participated in the two-day meeting and engaged in lively discussions.展开更多
Text mining, also known as discovering knowledge from the text, which has emerged as a possible solution for the current information explosion, refers to the process of extracting non-trivial and useful patterns from ...Text mining, also known as discovering knowledge from the text, which has emerged as a possible solution for the current information explosion, refers to the process of extracting non-trivial and useful patterns from unstructured text. Among the general tasks of text mining such as text clustering, summarization, etc, text classification is a subtask of intelligent information processing, which employs unsupervised learning to construct a classifier from training text by which to predict the class of unlabeled text. Because of its simplicity and objectivity in performance evaluation, text classification was usually used as a standard tool to determine the advantage or weakness of a text processing method, such as text representation, text feature selection, etc. In this paper, text classification is carried out to classify the Web documents collected from XSSC Website （http：//www.xssc.ac.cn）. The performance of support vector machine （SVM） and back propagation neural network （BPNN） is compared on this task. Specifically, binary text classification and multi-class text classification were conducted on the XSSC documents. Moreover, the classification results of both methods are combined to improve the accuracy of classification. An experiment is conducted to show that BPNN can compete with SVM in binary text classification; but for multi-class text classification, SVM performs much better. Furthermore, the classification is improved in both binary and multi-class with the combined method.展开更多
文摘The 595th session of the Xiangshan Science Conferences was recently held in Fragrant Hill Hotel,Beijing,on May 22-23,2017.The theme of this highly successful session was on＂Health Risk ad Control of Disinfection By-products（DBPs）in China＂.More than fifty prominent scientists from Australia,Canada,China,and the United States actively participated in the two-day meeting and engaged in lively discussions.
基金This work is supported by Ministry of Education, Culture, Sports, Science and Technology of Japan under the ＂Kanazawa Region, Ishikawa High-Tech Sensing Cluster of Knowledge-Based Cluster Creation Project＂ and the National Natural Science Foundation of China under Grant No.70571078 and 70221001.
文摘Text mining, also known as discovering knowledge from the text, which has emerged as a possible solution for the current information explosion, refers to the process of extracting non-trivial and useful patterns from unstructured text. Among the general tasks of text mining such as text clustering, summarization, etc, text classification is a subtask of intelligent information processing, which employs unsupervised learning to construct a classifier from training text by which to predict the class of unlabeled text. Because of its simplicity and objectivity in performance evaluation, text classification was usually used as a standard tool to determine the advantage or weakness of a text processing method, such as text representation, text feature selection, etc. In this paper, text classification is carried out to classify the Web documents collected from XSSC Website （http：//www.xssc.ac.cn）. The performance of support vector machine （SVM） and back propagation neural network （BPNN） is compared on this task. Specifically, binary text classification and multi-class text classification were conducted on the XSSC documents. Moreover, the classification results of both methods are combined to improve the accuracy of classification. An experiment is conducted to show that BPNN can compete with SVM in binary text classification; but for multi-class text classification, SVM performs much better. Furthermore, the classification is improved in both binary and multi-class with the combined method.
