Document Type

Article

Department or Administrative Unit

Computer Science

Publication Date

9-1-2010

Abstract

Neural networks have been applied successfully in many fields. However, satisfactory results can only be found under large sample conditions. When it comes to small training sets, the performance may not be so good, or the learning task can even not be accomplished. This deficiency limits the applications of neural network severely. The main reason why small datasets cannot provide enough information is that there exist gaps between samples, even the domain of samples cannot be ensured. Several computational intelligence techniques have been proposed to overcome the limits of learning from small datasets.

We have the following goals: i. To discuss the meaning of "small" in the context of inferring from small datasets. ii. To overview computational intelligence solutions for this problem. iii. To illustrate the introduced concepts with a real-life application.

Comments

This article was originally published Open Access in the International Journal of Computers, Communication & Control. The full-text article from the publisher can be found here.

Journal

International Journal of Computers, Communication & Control

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Rights

Copyright © 2006-2010 by CCC Publications

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