Subspace-based support vector machines for pattern classification.
Takuya KitamuraSyogo TakeuchiShigeo AbeKazuhiro FukuiPublished in: Neural Networks (2009)
Keyphrases
- pattern classification
- support vector
- learning machines
- large margin classifiers
- maximum margin
- feature extraction
- linear dimensionality reduction
- pattern recognition
- pattern classification problems
- classification accuracy
- support vector machine
- decision boundary
- low dimensional
- probabilistic neural network
- kernel function
- fuzzy classifier
- kernel methods
- locality preserving
- nearest neighbor rule
- high dimensional data
- principal component analysis
- feature space
- mass spectrometry data
- feature selection
- radial basis function neural network
- generalization ability
- dimensionality reduction
- linear discriminant analysis
- machine learning
- subspace methods
- principal components
- parzen window
- radial basis function
- high throughput
- high dimensional