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.

该文章提出了一种新颖的在线学习方法，旨在找到将数据点标记为正或负之间的分割超平面。该方法基于感知器算法，通过调整神经元权重来搜索分割超平面，使其更加高效。通过适当的数据集转换，该方法将数据的分离性问题减少为一类分类问题。实证结果表明在数据集大小超过数据维度时，该方法比感知器算法更有效。