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dc.contributor.authorKe, Yizhenen_US
dc.identifier.citationKe, Y. (2022). Open-set pattern recognition and its application in information extraction from text. Master's thesis, Nanyang Technological University, Singapore.
dc.description.abstractIn traditional supervised learning, the training set contains the same classes that appear in the testing set. However, the classifier may encounter previously unseen classes in the actual world, which is likely to create errors if a close-set classifier divides these data into the original category. The open-set classifier is designed to classify known samples accurately and reject unrelated samples. However, there are fewer applications in text classification. The goal of this paper is to achieve the application of open-set recognition on text classification tasks. This paper first reviews the work related to text classification and open-set classification identification. Subsequently, this paper determines the use of GloVe technique to map files to vector space. Considering that CNN and LSTM are superior in text classification, this article conducted a preliminary experiment and selected CNN with better performance as the base model. On this basis, SVDD and OpenMax methods are used in the 10 domains and 20 domains of the data set, respectively, and are compared with existing text classifiers. SVDD has similar training results to the currently open-set classifier based on SVM. The performance of OpenMax in the text classifier does not greatly vibrate by Openness and has good accuracy.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleOpen-set pattern recognition and its application in information extraction from texten_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorMao Kezhien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
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