Classification of Coronary Stenosis using a Support Vector Machine with Automatic Parameter Tuning.
Miguel-Angel Gil-RiosIván Cruz-AcevesClaire ChalopinJorge BrievaSandra L. Gomez CoronelErnesto Moya-AlborPublished in: SIPAIM (2023)
Keyphrases
- parameter tuning
- support vector machine
- support vector
- svm classifier
- support vector machine svm
- high classification accuracy
- ink bleed
- feature vectors
- svm classification
- classification accuracy
- decision boundary
- pattern recognition
- machine learning
- parameter settings
- classification method
- classification algorithm
- coronary artery
- training set
- model selection
- fully automatic
- image classification
- training samples
- soft margin
- kernel methods
- generalization ability
- feature selection
- small sample
- feature extraction
- structured output
- multi class
- multi class support vector machines
- neural network
- ct images
- coronary artery disease
- hyperplane
- decision trees
- simulated annealing
- knn
- feature space
- artificial neural networks