We describe an algorithm for automatically learning discriminative components of objects with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds ...
Bernd Heisele, Thomas Serre, Massimiliano Pontil, ...
This paper presents the design and implementation of an adaptive open-set speaker identification system with genetic learning classifier systems. One of the challenging problems i...
WonKyung Park, Jae C. Oh, Misty K. Blowers, Matt B...
We empirically evaluate several state-of-theart methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to se...
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of usin...
The KSU Willie entry in the Semantic Vision Challenge will use a variety of classifiers, some standard classifiers and some newly developed classifiers, to learn the classificatio...
David Gustafson, Aaron Chavez, Michael Marlen, And...