Fatigue level estimation of bill based on feature-selected acoustic energy pattern by using supervised SOM.
Masaru TeranishiSigeru OmatuToshihisa KosakaPublished in: CSTST (2008)
Keyphrases
- unsupervised learning
- feature vectors
- self organizing maps
- feature level
- supervised learning
- machine learning
- energy consumption
- accurate estimation
- estimation algorithm
- semi supervised
- energy minimization
- learning algorithm
- image features
- pattern matching
- parameter estimation
- higher level
- feature set
- input space
- pattern discovery
- high dimensional
- estimation process
- pairwise