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中文摘要：
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。
英文摘要：
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.
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同项目期刊论文
Dominance-based Rough Set Approach and Knowledge Reductions in Incomplete Ordered Information System
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