Tuning kernel function parameters of support vector machines for segmentation of lung disease patterns in high-resolution computed tomography images.
Alena ShamsheyevaArcot SowmyaPublished in: Medical Imaging: Image Processing (2004)
Keyphrases
- knn
- kernel function
- computed tomography images
- lung disease
- support vector
- support vector machine
- kernel parameters
- high resolution
- automatic segmentation
- large margin classifiers
- learning machines
- kernel methods
- input space
- ground glass opacity
- feature selection
- feature space
- support vectors
- hyperparameters
- svm classification
- support vector regression
- segmentation algorithm
- polynomial kernels
- reproducing kernel hilbert space
- positive definite
- high dimensional feature space
- hyperplane
- svm classifier
- loss function
- high dimensional
- kernel matrix
- medical images
- kernel learning
- kernel machines
- image analysis
- multiple kernel learning
- gaussian kernels
- rbf kernel
- gaussian processes
- magnetic resonance images
- remote sensing
- cross validation
- optimal kernel
- data sets
- data mining
- level set
- image processing
- maximum margin
- classification accuracy
- feature set
- x ray