A decision boundary hyperplane for the vector space of conics using a polinomial kernel in m-Euclidean space.
Isidro B. NietoRefugio VallejoPublished in: IJCNN (2008)
Keyphrases
- euclidean space
- hyperplane
- vector space
- decision boundary
- kernel function
- reproducing kernel hilbert space
- support vector
- feature space
- linear classifiers
- input space
- low dimensional
- riemannian manifolds
- data points
- feature vectors
- training samples
- support vector machine
- kernel methods
- finite dimensional
- support vectors
- similarity search
- metric space
- distance measure
- principal components
- svm classifier
- kernel matrix
- high dimensional data
- dimensionality reduction
- high dimensional
- infinite dimensional
- image processing
- positive definite
- convex hull
- k nearest neighbor
- supervised learning
- multi class
- similarity measure
- feature extraction
- feature selection
- learning algorithm