Multi-objective neural network optimization for visual object detection
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
In real-time computer vision, there is a need for classifiers that detect patterns fast and reliably. We apply multi-objective optimization (MOO) to the design of feed-forward neural networks for real-world object recognition tasks, where computational complexity and accuracy define partially conflicting objectives. Evolutionary structure optimization and pruning are compared for the adaptation of the network topology. In addition, the results of MOO are contrasted to those of a single-objective evolutionary algorithm. As a part of the evolutionary algorithm, the automatic adaptation of operator probabilities in MOO is described.
Original language | English |
---|---|
Title of host publication | Multi-objective machine learning |
Editors | Yaochu Jin |
Number of pages | 27 |
Volume | V |
Publication date | 2006 |
Pages | 629-655 |
ISBN (Print) | 978-3-540-30676-4 |
ISBN (Electronic) | 978-3-540-33019-6 |
DOIs | |
Publication status | Published - 2006 |
Externally published | Yes |
Series | Studies in Computational Intelligence |
---|---|
Volume | 16 |
ISSN | 1860-949X |
ID: 168323444