We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labele...
We present an experimental framework for Entity Mention Detection in which two different classifiers are combined to exploit Data Redundancy attained through the annotation of a l...
The optimization method is one of key issues in discriminative learning of pattern classifiers. This paper proposes a hybrid approach of the Covariance Matrix Adaptation Evolution...
In this paper, we propose a framework to carry out supervised classification of images containing both textured and non textured areas. Our approach is based on active contours. U...
In the field of pattern recognition, the concept of Multiple Classifier Systems (MCSs) was proposed as a method for the development of high performance classification systems. At ...