In a typical online learning scenario, a learner is required to process a
large data stream using a small memory buffer. Such a requirement is usually in
conflict with a learner's primary pursuit of prediction accuracy. To address
this dilemma, we introduce a novel Bayesian online classi cation algorithm,
called the Virtual Vector Machine. The virtual vector machine allows you to
smoothly trade-off prediction accuracy with memory size. The virtual vector
machine summarizes the information contained in the preceding data stream by a
Gaussian distribution over the classi cation weights plus a constant number of
virtual data points. The virtual data points are designed to add extra
non-Gaussian information about the classi cation weights. To maintain the
constant number of virtual points, the virtual vector machine adds the current
real data point into the virtual point set, merges two most similar virtual
points into a new virtual point or deletes a virtual point that is far from the
decision boundary. The information lost in this process is absorbed into the
Gaussian distribution. The extra information provided by the virtual points
leads to improved predictive accuracy over previous online classification
algorithms.

提出了一种基于贝叶斯定理的虚拟支持向量机算法，通过使用高斯分布和虚拟数据点，平衡在线学习场景下的分类预测精度和内存缓冲的数据处理要求。