一种基于局部敏感哈希的SVM快速增量学习算法

为了提高大规模高维度数据的训练速度和分类精度,提出了一种基于局部敏感哈希的SVM快速增量学习方法。算法首先利用局部敏感哈希能快速查找相似数据的特性,在SVM算法的基础上筛选出增量中可能成为SV的样本,然后将这些样本与已有SV一起作为后续训练的基础。使用多个数据集对该算法进行了验证。实验表明,在大规模增量数据样本中,提出的SVM快速增量学习算法能有效地提高训练学习的速度,并能保持有效的准确率。

Fast Incremental Learning Algorithm of SVM with Locality Sensitive Hashing

In order to improve the training speed and the classification accuracy in large scale high dimension data,a new incremental learning algorithm of SVM with LSH was proposed.It uses the LSH algorithm,which can seek similar data fast in a large scale and high dimension data,to filter out the incremental samples which may become SVs on the basis of the SVM algorithm.Then it makes the selected samples and the existing SVs as a basis for the following training.We took advantages of the multiple data sets to validate the algorithm.Experiments show that this new algorithm can improve the speed of the incremental training learning in large scale data with the effective accuracy.