We introduce a new state discrimination problem in which we are given additional information about the state after the measurement, or more generally, after a quantum memory bound ...
Manuel A. Ballester, Stephanie Wehner, Andreas Win...
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...
Accurate, well-calibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a cost-sensitive decision must be mad...
: This paper proposes twin prototype support vector machine (TVM), a constant space and sublinear time support vector machine (SVM) algorithm for online learning. TVM achieves its ...
We observed that for multimedia data – especially music - collaborative similarity measures perform much better than similarity measures derived from content-based sound feature...