Deterministic solutions are becoming more critical for interpretability. Weighted Least-Squares (WLS) has been widely used as a deterministic batch solution with a specific weight design. In the online settings of WLS, exact reweighting is necessary to converge to its batch settings. In order to comply with its necessity, the iteratively reweighted least-squares algorithm is mainly utilized with a linearly growing time complexity which is not attractive for online learning. Due to the high and growing computational costs, an efficient online formulation of reweighted least-squares is desired. We introduce a new deterministic online classification algorithm of WLS with a constant time complexity for binary class rebalancing. We demonstrate that our proposed online formulation exactly converges to its batch formulation and outperforms existing state-of-the-art stochastic online binary classification algorithms in real-world data sets empirically.

翻译：对可解释性来说,确定性的方法越来越重要。 加权最低方块(WLS)已被广泛用作具有特定重量设计的确定性批量解决方案。 在WLS的在线设置中,需要精确的重新加权才能与批量设置趋同。 为了符合其必要性,迭代再加权最低方方块算法主要以线性增长的时间复杂性来使用,而这对在线学习并不具有吸引力。 由于计算成本高且不断增长,因此需要高效的重新加权最低方块在线配方。 我们引入一种新的确定性在线分类算法,对二元级再平衡具有恒定的时间复杂性。 我们证明,我们提议的在线配方与其批量配方完全一致,超过了现实世界数据中现有的最新随机在线二元分类算法。