改进的概率选择主动支持向量机算法

针对大多数主动学习支持向量机（ASVM）的主动学习策略只注重考察超平面附近的样本，忽略了有些距离超平面远但是支持向量的样本，而且没有考虑当前超平面是否接近实际的超平面。提出一种基于概率的主动支持向量机算法，采用一个置信因子来衡量当前的超平面接近实际的超平面的程度。实验结果都验证了该算法在分类精度与计算量方面都有了较大改进。

Improved algorithm of active learning support vector machine based on probability

While most existing methods of ASVM are focus on the samples which are close to the current separating hyper-plane,and it ignores some SV samples which are far form the separating hyperplane,also it doesnt’ consider on if the cur-rent separating hyperplane is close to the optimal one.In order to make up for these shortagest,his paper presents a new classification method of ASVM based on probability.And it not only presents a new method of probability,but also mea-sures the degree of closeness of the current separating hyperplane to the actual separating hyperplane by a confidence factor.Experimental results verify the improvement of the proposed method both in term of classification precision and computation.