国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

For the large data environment, there are many redundancy and noisy data, resulting in storage costs and poor learning accuracy problems, in order to effectively select representative samples, while improving learning accuracy and reduce training time, the thesis presents a selective sampling of SVM incremental learning algorithm, based on markov sampling as a sampling method. In the sampling process, the decision-making model is used to calculate the transition probability between samples, and then the transition probability is adopted to decide whether to accept the sample as the training data, in order to select the representative sample. Compared with other SVM incremental learning algorithms, the experiment selects 9 benchmark data sets and uses 10-fold cross-validation to select regularization parameters, and the numerical experiments show that the algorithm can greatly reduce the total time of sampling and training and the number of support vectors while improving learning accuracy.