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By de la Pena J.A., Vallejo E., Atakishiyev N. (eds.)

ISBN-10: 0821836307

ISBN-13: 9780821836309

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The line indicates the number of documents tested. The top 1000 most common n-grams are used for classification. Fig. 5. D. Hansen et al. Fig. 6. Same as in Figure 4, but here only for high-grade students (15 out of 30 students) Fig. 7. 4 seconds. 4 seconds. With 16 documents, the amount of training 51 test data increased by 320%, but the computation time increased by 692%. The exact computational complexity of the LIBSVM SVM version is not known, but it is according to Hsu et al. [4] not linear, as the results above also indicate.

Authorship attribution methods are commonly used in digital learning management systems to detect student submissions whose writing style seems to depart from the student’s previous work. These systems build and continuously update an authorial fingerprint for each student, based on all his incoming writings; when a newly submitted document deviates notably from the author’s fingerprint, the system alerts the teacher. There are two main challenges associated with the task of authorship attribution in the domain of plagiarism detection for secondary education.

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Algebraic Structures and Their Representations by de la Pena J.A., Vallejo E., Atakishiyev N. (eds.)

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