In kernel density estimation methods, an approximation of the data probability density function is achieved by locating a kernel function at each data location. The smoothness of ...
The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to a...
A hardware method for functional unit assignment is presented, based on the principle that a functional unit’s power consumption is approximated by the switching activity of its...
Steve Haga, Natasha Reeves, Rajeev Barua, Diana Ma...
The median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Ne...
Miquel Ferrer, Dimosthenis Karatzas, Ernest Valven...
We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation ...