CAJ | 학술논문

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时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.
점진지지향량결정점진지지향량궤적범화능력,기선취지관중요.대차제출료일충신영적기우개솔계산적점진지지향량선취책략.해방법위매개양본점구조일개통과해양본점적합괄분리면,해양본점성위점진지지향량적개솔시근거해분리면시량류양본적분리솔래고계적.구유교고개솔치적훈련양본피선위점진지지향량,용이훈련화경신점진지지향량궤.비교성적실험표명,해방법재보지점진지지향량궤범화능력적전제하,재훈련효솔상구유비상돌출적우세.
Incremental support vectors determine the generalization performance of incremental support vector machine,SO their choosing is significant.We proposed fl 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.