.png){kind=icon}
.png){kind=icon}
Return
An efficient active set method for optimization extreme learning machines
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.01.092.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper an efficient active set algorithm is presented for fast training of Optimization Extreme Learning Machines (OELMs). This algorithm suggests the use of an efficient identification technique of active set and the value reassignment technique for quadratic programming problem. With these strategies, this algorithm is able to drop many constraints from the active set at each iteration, and it can converge to the optimal solution with less iterations. The global convergence properties of the algorithm as well as its theoretical properties are analyzed. The effectiveness of the algorithm is demonstrated via benchmark datasets from many sources. Experiment results indicate that the quadratic programming problem which keeps the number of constraints in the active set as small as possible is computationally most efficient. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Optimization extreme learning machines
Quadratic programming
Active set
Piecewise projected gradient
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
No organization information available