Improved adaptive pruning algorithm for least squares support vector regression

As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the existing adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satisfactory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which involves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generalization performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.