计算机科学 ›› 2010, Vol. 37 ›› Issue (2): 200-202.
• 人工智能 • 上一篇 下一篇
申丰山,马玉军,张军英
出版日期:
发布日期:
SHEN Feng-shan,MA Yu-jun,ZHANG Jun-ying
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摘要: 渐进支持向量决定渐进支持向量机的泛化能力,其选取至关重要。对此提出了一种新颖的基于概率计算的渐进支持向量选取策略。该方法为每个样本点构造一个通过该样本点的合适分离面,该样本点成为渐进支持向量的概率是根据该分离面对两类样本的分离率来估计的。具有较高概率值的训练样本被选为渐进支持向量,用以训练和更新渐进支持向量机。比较性的实验表明,该方法在保持渐进支持向量机泛化能力的前提下,在训练效率上具有非常突出的优势。
关键词: 渐进支持向量机,渐进支持向量,支撑分离面,支撑分离率
Abstract: Incremental support vectors determine the generalization performance of incremental support vector machine,so their choosing is significant We proposed a novel strategy to choose the incremental support vectors by computing their probabilities. The method constructs an appropriate separating hyperplane for each training sample, which goes through the corresponding training sample point. And then the probability of a sample to be incremental support vector is estimated by the separating rate of the corresponding hyperplane. Those samples with higher probabilities are chosen as the incremental support vectors to train and update the incremental support vector machine. Comparative numerical experiments of our method against the existing methods show that our method has outstanding advantage in the training efficiency without deteriorating the generalization performance of incremental support vector machine.
Key words: Incremental support vector machine, Incremental support vector, Support separating hyperplane, Support separating rate
申丰山,马玉军,张军英. 渐进支持向量的一种新颖选取策略[J]. 计算机科学, 2010, 37(2): 200-202. https://doi.org/
SHEN Feng-shan,MA Yu-jun,ZHANG Jun-ying. Novel Strategy for Selecting Incremental Support Vectors[J]. Computer Science, 2010, 37(2): 200-202. https://doi.org/
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