Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation.
Stefano Di CarloMatteo FalasconiErnesto SánchezAlberto SciontiGiovanni SquilleroAlberto Paolo TondaPublished in: Pattern Recognit. Lett. (2011)
Keyphrases
- recognition accuracy
- recognition rate
- face recognition
- pattern matching
- improve the recognition rate
- evolutionary computation
- genetic algorithm
- sensor networks
- pattern discovery
- concept drift
- elastic graph matching
- neural network
- evolutionary optimization
- real time
- object detection
- computational intelligence
- multi objective
- pattern detection
- chemical reaction