.png){kind=icon}
Return
An efficient multi-objective learning algorithm for RBF neural network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2010.06.022.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Most of modern multi-objective machine learning methods are based on evolutionary optimization algorithms. They are known to be global convergent, however, usually deliver nondeterministic results. In this work we propose the deterministic global solution to a multi-objective problem of supervised learning with the methodology of nonlinear programming. As the result, the proposed multi-objective algorithm performs a global search of Pareto-optimal hypotheses in the space of RBF networks, determining their weights and basis functions. In combination with the Akaike and Bayesian information criteria, the algorithm demonstrates a high generalization efficiency on several synthetic and real-world benchmark problems. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Multi-objective learning
Radial-basis functions
Pareto-optimality
Model selection
Regularization
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
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSaveShareSaveShareSave
View more