concentrations by Support Vector Machines and neural networks using Principal Component Analysis.
István JuhosLászló MakraBalázs TóthPublished in: Simul. Model. Pract. Theory (2008)
Keyphrases
- neural network
- principal component analysis
- support vector
- learning machines
- large margin classifiers
- linear discriminant analysis
- principal components
- pattern recognition
- training algorithm
- discriminant analysis
- generalization ability
- dimensionality reduction
- face recognition
- dimension reduction
- independent component analysis
- radial basis function
- support vector machine
- low dimensional
- fuzzy logic
- kernel function
- artificial neural networks
- genetic algorithm
- covariance matrix
- back propagation
- logistic regression
- generalization bounds
- neural network model
- loss function
- binary classification
- multi class
- classification accuracy
- computer vision
- singular value decomposition
- cross validation
- face images
- neural classifier
- feature extraction
- support vector regression
- multi layer
- training process
- machine learning
- multi class classification
- soft margin
- dimension reduction methods
- support vectors
- feed forward
- kernel methods
- self organizing maps
- svm classifier
- feature space
- training data
- feature selection