퍼지및지능시스템학회 논문지
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| 한글제목(Korean Title) |
In this paper, a control algorithm suitable for multi-objective control problems is proposed based on the |
| 영문제목(English Title) |
In this paper, a control algorithm suitable for multi-objective control problems is proposed based on the |
| 저자(Author) |
Jangmin O
Byoung-Tak Zhang
|
| 원문수록처(Citation) |
VOL 10 NO. 04 PP. 0374 ~ 0378 (2000. 08) |
한글내용
(Korean Abstract) |
null |
영문내용
(English Abstract) |
In information retrieval, lack of positive examples is a main cause of poor performance. In this case most
learning algorithms may not capture proper characteristics in the data leading to low recall. To solve the
problem of unbalanced data, we propose a boosting method that uses linear perceptrons as weak learners. The
perceptrons are trained on local data sets. The proposed algorithm is applied to a text filtering problem for
which only a small portion of positive examples is available. In the experiment on category crude of the
Reuters-21578 document set, the boosting method achieved the recall of 80.8%, which is 37.2% improvement
over multilayer perceptrons with comparable precision. |
| 키워드(Keyword) |
multi-objective control problems
|
| 파일첨부 |
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