期刊
APPLIED ARTIFICIAL INTELLIGENCE
卷 34, 期 6, 页码 515-536出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/08839514.2020.1730623
关键词
-
Online learning makes sequence of decisions with partial data arrival where next movement of data is unknown. In this paper, we have presented a new technique as multiple times weight updating that update the weight iteratively for same instance. The proposed technique analyzed with popular state-of-art algorithms from literature and experimented using established tool. The results indicate that mistake rate reduces to zero or close to zero for various datasets and algorithms. The overhead running cost is not too expensive and achieving mistake rate close to zero further strengthens the proposed technique. The present work includes bound nature of weight updating for single instance and achieve optimal weight value. This proposed work could be extended to big datasets problems to reduce mistake rate in online learning environment. Also, the proposed technique could be helpful to meet real life challenges.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据