Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, ...
: In this paper, we present an approach for writer identification carried out using off-line Arabic handwriting. Our proposed method is based on the combination of global and struc...
We propose a probabilistic graphical model to represent weakly annotated images1 . This model is used to classify images and automatically extend existing annotations to new image...
We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...
Matthew T. Karnick, Michael Muhlbaier, Robi Polika...
This paper introduces a geometrically inspired large-margin classifier that can be a better alternative to the Support Vector Machines (SVMs) for the classification problems with ...