TSVM (transductive support vector machines) tries to minimize misclassification of these particular examples based on a particular test set. It is more practical and performs well on classification. In this paper, a progressive SVM is introduced briefly and an improved algorithm for text classification named double transductive inference algorithm based on TSVM is presented in detail. The experimental results on e-mail classification show that this improved algorithm is effective on a mixed training set of a small number of unlabeled examples and a large number of labeled examples

The method of quantum dynamics is employed to investigate the mean strategy in the swarm intelligence algorithm. The physical significance of the population mean point is explained as the location where the optimal solution with the highest likelihood can be found once a quantum system has reached a ground state. Through the use of the double well function and the CEC2013 test suite, controlled experiments are conducted to perform a comprehensive performance analysis of the mean strategy. The empirical results indicate that implementing the mean strategy not only enhances solution diversity but also yields accurate, efficient, stable, and effective outcomes for finding the optimal solution.