Hybrid machine learning to improve predictive performance

被引：0

作者：

Ha, Sung Ho ^{[1
]}

Jin, Jong Sik ^{[1
]}

Joo, Seong Hyeon ^{[1
]}

机构：

[1] Kyungpook Natl Univ, Sch Business Adm, 1370 Sangyeok Dong, Taegu, South Korea

来源：

COMPUTATIONAL METHODS AND APPLIED COMPUTING | 2008年

关键词：

hybrid machine learning; case-based reasoning; data mining prediction; feature weighting; SEMICONDUCTOR; PATTERNS; SYSTEM; YIELD;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Yield management in semiconductor manufacturing companies requires accurate yield prediction and continual control. This paper presents a hybrid method of combining machine learning techniques to detect high and low yields. In the real applications, the hybrid method provides more accurate yield prediction than other methods.

引用

页码：373 / +

页数：2

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