Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensiona...
Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on...
In many real-world classification problems the input contains a large number of potentially irrelevant features. This paper proposes a new Bayesian framework for determining the r...
Yuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picar...
In recent years, large databases of natural images have
become increasingly popular in the evaluation of face and
object recognition algorithms. However, Pinto et al. previously
...
Background: The large gap between the number of protein sequences in databases and the number of functionally characterized proteins calls for the development of a fast computatio...