We consider the problem of numerical stability and model density growth when training a sparse linear model from massive data. We focus on scalable algorithms that optimize certain...
Abstract. Ontology classification--the computation of subsumption hierarchies for classes and properties--is one of the most important tasks for OWL reasoners. Based on the algorit...
Birte Glimm, Ian Horrocks, Boris Motik, Giorgos St...
Within the field of action recognition, features and descriptors are often engineered to be sparse and invariant to transformation. While sparsity makes the problem tractable, it ...
It has been shown in prior work in management science, statistics and machine learning that using an ensemble of models often results in better performance than using a single ‘...
Object/scene detection by discriminative kernel-based classification has gained great interest due to its promising performance and flexibility. In this paper, unlike traditional ...