This paper presents a novel online learning method that aims at finding a separator hyperplane between data points labelled as either positive or negative. Since weights and biases of artificial neurons can directly be related to hyperplanes in high-dimensional spaces, the technique is applicable to train perceptron-based binary classifiers in machine learning. In case of large or imbalanced data sets, use of analytical or gradient-based solutions can become prohibitive and impractical, where heuristics and approximation techniques are still applicable. The proposed method is based on the Perceptron algorithm, however, it tunes neuron weights in just the necessary extent during searching the separator hyperplane. Due to an appropriate transformation of the initial data set we need not to consider data labels, neither the bias term. respectively, reducing separability to a one-class classification problem. The presented method has proven converge; empirical results show that it can be more efficient than the Perceptron algorithm, especially, when the size of the data set exceeds data dimensionality.

翻译：本文提出一种新颖的在线学习方法，旨在寻找标记为正类或负类的数据点之间的分离超平面。由于人工神经元的权重和偏置可直接关联至高维空间中的超平面，该技术适用于机器学习中基于感知器的二分类器训练。对于大规模或非平衡数据集，基于解析或梯度的方法可能变得过于复杂且不实用，而启发式及近似技术仍可适用。所提方法基于感知器算法，但在搜寻分离超平面过程中仅对神经元权重进行必要程度的调整。通过对初始数据集进行适当变换，我们无需考虑数据标签及偏置项，从而将可分性简化为单类分类问题。该方法已被证明收敛；实验结果表明，当数据集规模超过数据维度时，其效率可能优于感知器算法。