Online learning using multiple times weight updating

TL;DR

This paper introduces a multiple times weight updating technique for online learning, which iteratively updates weights for each data instance, significantly reducing mistake rates with manageable computational costs.

Contribution

It proposes a novel multiple times weight updating method that improves mistake rates in online learning and analyzes its theoretical bounds and practical performance.

Findings

Mistake rate reduces to near zero across datasets.

The technique has low additional computational overhead.

Achieves near-optimal weight values for each instance.

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 forsame instance. The proposed technique analyzed with popular state-of-art algorithms from literature and experimented using established tool. The results indicates 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 strengthen the proposed technique. The present work include 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.