Name
Abstract | ||
|---|---|---|
Self-awareness is a kind of ability of recognizing oneself as an individual being different from the environment and other individuals. This paper proposes negative correlation learning with self-awareness in order for each artificial neural network (ANN) in a committee machine to be self-aware in learning so that it could decide by itself to learn more or less. On one hand, when the learning would force itself to be closer to the ensemble, an individual ANN would choose to learn less so that the learning on that direction would be disencouraged. On the other hand, when the learning would help itself to be more different to the ensemble, an individual ANN would let itself to learn more so that the learning on that direction would be encouraged. It is expected that such ANNs being aware of their own behavior and performance can manage trade-offs between goals at run-time. Such self-awareness enables a committee machine to better meet their requirements for predictions on the unknown data. Measurement results have been presented to how self-awareness could support the different behaviors and maintain the performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICMLC.2016.7873004 | 2016 International Conference on Machine Learning and Cybernetics (ICMLC) |
Keywords | Field | DocType |
Neural network ensembles,Negative correlation learning,Private awareness | Negative correlation,Active learning (machine learning),Committee machine,Computer science,Self-awareness,Boosting (machine learning),Artificial intelligence,Error-driven learning,Artificial neural network,Machine learning,Cybernetics | Conference |
Volume | ISBN | Citations |
2 | 978-1-5090-0391-4 | 0 |
PageRank | References | Authors |
0.34 | 5 | 1 |