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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.20 No.2
- 발행연도
- 2020.6
- 수록면
- 145 - 155 (11page)
- DOI
- 10.5391/IJFIS.2020.20.2.145
이용수
초록· 키워드
오류제보하기
In this study, a newly designed local model called locally weighted regression model is proposed for the regression problem. This model predicts the output for a newly submitted data point. In general, the local regression model focuses on an area of the input space specified by a certain kernel function (Gaussian function, in particular). The local area is defined as a region enclosed by a neighborhood of the given query point. The weights assigned to the local area are determined by the related entries of the partition matrix originating from the fuzzy C-means method. The local regression model related to the local area is constructed using a weighted estimation technique. The model exploits the concept of the nearest neighbor, and constructs the weighted least square estimation once a new query is provided given. We validate the modeling ability of the overall model based on several numeric experiments.
목차
Abstract
1. Introduction
2. Locally Weighted Regression with k-NN Approach
3. Lazy Learner for Local Regression based on Neighborhood Information
4. Experimental Studies
5. Conclusions
References
참고문헌 (25)
참고문헌 신청
H. A. Guvenir / 2000 / Regression on feature projections / Knowledge-Based Systems 13(4):207~214
G. Bontempi / 2001 / The local paradigm for modeling and control : from neuro-fuzzy to lazy learning / Fuzzy Sets and Systems 121(1):59~72
J. Yen / 1998 / Improving the interpretability of TSK fuzzy models by combining global learning and local learning / IEEE Transactions on Fuzzy Systems 6(4):530~537
D. W. Aha / 1998 / Feature Extraction, Construction and Selection / Springer :13~32
F. Dornaika / 2013 / Exponential local discriminant embedding and its application to face recognition / IEEE Transactions on Cybernetics 43(3):921~934
G. Bontempi / 2001 / The local paradigm for modeling and control : from neuro-fuzzy to lazy learning / Fuzzy Sets and Systems 121(1):59~72
J. Yen / 1998 / Improving the interpretability of TSK fuzzy models by combining global learning and local learning / IEEE Transactions on Fuzzy Systems 6(4):530~537
D. W. Aha / 1998 / Feature Extraction, Construction and Selection / Springer :13~32
F. Dornaika / 2013 / Exponential local discriminant embedding and its application to face recognition / IEEE Transactions on Cybernetics 43(3):921~934
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