TY - GEN
T1 - Extreme Learning Machine Based on Adaptive Matrix Iteration
AU - Li, Yuxiang
AU - Zou, Weidong
AU - Wang, Can
AU - Xia, Yuanqing
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Under the continuous optimization and development of various algorithms in machine learning, the performance of the algorithm model on classification and regression prediction problems has become an important evaluation metric for the quality of algorithms. In order to solve the problems of low testing accuracy and unsatisfactory generalization performance of the models trained by the traditional extreme learning machine, this paper proposes an extreme learning machine algorithm based on adaptive convergence factor matrix iteration. This algorithm optimizes the calculation method of solving the hidden layer output weight matrix, while retaining the network structure model of the traditional extreme learning machine. This algorithm is implemented with a matrix iterative method that includes an adaptive convergence factor to compute the output weight matrix. As a result, it can adaptively select the optimal convergence factor according to the structure of the iterative equations, and thus use iterative method to solve linear equations efficiently and accurately upon ensuring the convergence of the equations. The experiment results show that the proposed algorithm has better performance in model training efficiency and testing accuracy, compared with the traditional extreme learning machine, the support vector machine, and other algorithms for data classification and regression prediction.
AB - Under the continuous optimization and development of various algorithms in machine learning, the performance of the algorithm model on classification and regression prediction problems has become an important evaluation metric for the quality of algorithms. In order to solve the problems of low testing accuracy and unsatisfactory generalization performance of the models trained by the traditional extreme learning machine, this paper proposes an extreme learning machine algorithm based on adaptive convergence factor matrix iteration. This algorithm optimizes the calculation method of solving the hidden layer output weight matrix, while retaining the network structure model of the traditional extreme learning machine. This algorithm is implemented with a matrix iterative method that includes an adaptive convergence factor to compute the output weight matrix. As a result, it can adaptively select the optimal convergence factor according to the structure of the iterative equations, and thus use iterative method to solve linear equations efficiently and accurately upon ensuring the convergence of the equations. The experiment results show that the proposed algorithm has better performance in model training efficiency and testing accuracy, compared with the traditional extreme learning machine, the support vector machine, and other algorithms for data classification and regression prediction.
KW - Adaptive convergence factor
KW - Data classification
KW - Extreme learning machine
KW - Machine learning
KW - Matrix iteration
KW - Model optimization
UR - http://www.scopus.com/inward/record.url?scp=85134685785&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-09726-3_16
DO - 10.1007/978-3-031-09726-3_16
M3 - Conference contribution
AN - SCOPUS:85134685785
SN - 9783031097256
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 177
EP - 188
BT - Advances in Swarm Intelligence - 13th International Conference, ICSI 2022, Proceedings, Part II
A2 - Tan, Ying
A2 - Shi, Yuhui
A2 - Niu, Ben
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Swarm Intelligence, ICSI 2022
Y2 - 15 July 2022 through 19 July 2022
ER -