It is common in classification methods to first place data in a vector space and then learn decision boundaries. We propose reversing that process: for fixed decision boundaries, ...
The decomposition method is currently one of the major methods for solving the convex quadratic optimization problems being associated with support vector machines. For a special c...
A universal data model, named DG, is introduced to handle vectorized data uniformly during the whole recognition process. The model supports low level graph algorithms as well as h...
We have derived a new algorithm for dictionary learning for sparse coding in the ℓ1 exact sparse framework. The algorithm does not rely on an approximation residual to operate, b...
In this paper we provide several new results concerning word and matrix semigroup problems using counter automaton models. As a main result, we prove a new version of Post's c...