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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.4 No.2
- 발행연도
- 2010.6
- 수록면
- 110 - 127 (18page)
이용수
초록· 키워드
오류제보하기
In this study, we propose a method for encoding documents into string vectors, instead of numerical vectors. A traditional approach to text categorization usually requires encoding documents into numerical vectors. The usual method of encoding documents therefore causes two main problems: huge dimensionality and sparse distribution. In this study, we modify or create machine learning-based approaches to text categorization, where string vectors are received as input vectors, instead of numerical vectors. As a result, we can improve text categorization performance by avoiding these two problems.
목차
1. INTRODUCTION
2. PREVIOUS WORKS
3. STRING VECTORS
4. String Vector-based Approaches
5. COMPARISON OF STRING VECTORS AND NUMERICAL VECTORS
6. CONCLUSIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2010-569-002480421