Transfer learning aims to solve new learning problems by extracting and making use of the common knowledge found in related domains. A key element of transfer learning is to ident...
Qiang Yang, Vincent Wenchen Zheng, Bin Li, Hankz H...
In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled ...
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning ML...
We consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. 'similar' point pairs. We define the similarity in informa...
This paper overviews a new gesture recognition framework
based on learning local motion signatures (LMSs) introduced
by [1]. After the generation of these LMSs computed
on one i...