ABSTRACT 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.

中文翻译：

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使用多次权重更新的在线学习

摘要 在线学习通过部分数据到达来做出决策序列，其中下一次数据移动是未知的。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 在本文中，我们提出了一种新技术，即多次权重更新，它为同一实例迭代更新权重。所提出的技术使用文献中流行的最先进算法进行分析，并使用已建立的工具进行实验。结果表明，对于各种数据集和算法，错误率降低到零或接近于零。开销运行成本并不太昂贵，并且实现接近于零的错误率进一步加强了所提出的技术。目前的工作包括单实例权重更新的边界性质，并实现最佳权重值。这项提议的工作可以扩展到大数据集问题，以降低在线学习环境中的错误率。还，