We propose a novel boosting algorithm which improves on current algorithms for weighted voting classification by striking a better balance between classification accuracy and the ...
We claim that divisible residuated lattices (DRLs) can act as a unifying evaluation framework for soft constraint satisfaction problems (soft CSPs). DRLs form the algebraic semanti...
Except in the case of normal (i.e Gaussian) distribution, it is very difficult to calculate the marginal probability distribution of ARMA signals. By using a particular form of mo...
This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian...
We propose a unified framework for deriving and studying soft-in soft-out (SISO) detection in multiple-access channels using the concept of variational inference. The proposed fram...