Combining sparseness and smoothness improves classification accuracy and interpretability.
Matthew de BrechtNoriko YamagishiPublished in: NeuroImage (2012)
Keyphrases
- classification accuracy
- cost function
- feature space
- multiple classifier systems
- feature set
- training data
- decision making
- feature selection
- databases
- image sequences
- data structure
- learning environment
- support vector
- data sets
- training set
- multiresolution
- knowledge base
- naive bayes
- feature subset
- terms of classification accuracy