Learning Rate for Convex Support Tensor Machines.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 32833645.
- Also identified by DOI 10.1109/TNNLS.2020.3015477.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Tensors are increasingly encountered in prediction problems. We extend previous results for high-dimensional least-squares convex tensor regression to classification problems with a hinge loss and establish its asymptotic statistical properties. Based on a general convex decomposable penalty, the rate depends on both the intrinsic dimension and the Rademacher complexity of the class of linear functions of tensor predictors.