Ontology highlight
ABSTRACT:
SUBMITTER: Cho H
PROVIDER: S-EPMC7434093 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
Proceedings of machine learning research 20190401
Representing data in hyperbolic space can effectively capture latent hierarchical relationships. To enable accurate classification of points in hyperbolic space while respecting their hyperbolic geometry, we introduce hyperbolic SVM, a hyperbolic formulation of support vector machine classifiers, and describe its theoretical connection to the Euclidean counterpart. We also generalize Euclidean kernel SVM to hyperbolic space, allowing nonlinear hyperbolic decision boundaries and providing a geome ...[more]