Linear dimensionality reduction (LDR) is quite important in pattern recognition due to its efficiency and low computational complexity. In this paper, we extend the two-class Chern...
A novel approach to classification is proposed in which a Paretobased ranking of individuals is used to encourage multiple individuals to participate in the solution. To do so, th...
This paper presents a novel approach to single-frame pedestrian classification and orientation estimation. Unlike previous work which addressed classification and orientation sepa...
The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. ...
We present a data-driven variant of the LR algorithm for dependency parsing, and extend it with a best-first search for probabilistic generalized LR dependency parsing. Parser act...