Least-Squares Support Vector Machine for Semi-Supervised Multi-Tasking

The semi-supervised multi-tasking using least-squares support vector machine can further improve performance by using related information of related tasks, and it inherits the advantages of high training speed and high efficiency of the least square support vector machine. Standard support vector machine is based on supervised learning, and it is necessary to manually mark large amounts of data for obtaining sufficient training data, which is costly and inefficient. In this paper, we apply least squares support vector machine based on semi-supervised learning to the multi-tasks and propose a semi-supervised multi-tasking approach using least-squares support vector machine. Based on related tasks learning simultaneously, multi-task least-squares support vector machine is used to train both labeled and unlabeled samples, overcoming the limitation of slow training, and using the useful information among related tasks to improve the efficiency of all tasks. In the training process, the regional tagging and the tag reset methods are used to reduce the number of iterations to achieve convergence and increases the fault tolerance rate. The experiment on the actual dataset shows the effectiveness of the approach.