Conference paper
Predicting knowledge in an ontology stream
Freddy Lécué, Jeff Z. Pan
IJCAI 2013
Let X be a data matrix of rank ρ, representing n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1- norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability, the margin and minimum enclosing ball in the feature space are preserved to within ε-relative error, ensuring comparable generalization as in the original space. We present extensive experiments with real and synthetic data to support our theory.
Freddy Lécué, Jeff Z. Pan
IJCAI 2013
Joseph Y. Halpern
aaai 1996
Joxan Jaffar
Journal of the ACM
Kellen Cheng, Anna Lisa Gentile, et al.
EMNLP 2024