This paper describes an approach to surface identification in the context of mobile robotics, applicable to supervised and unsupervised learning. The identification is based on ana...
Reliable estimation of the classification performance of learned predictive models is difficult, when working in the small sample setting. When dealing with biological data it is ...
Antti Airola, Tapio Pahikkala, Willem Waegeman, Be...
We present a novel method for unsupervised classification, including the discovery of a new category and precise object and part localization. Given a set of unlabelled images, som...
Leonid Karlinsky, Michael Dinerstein, Dan Levi, Sh...
A system that could automatically analyze the facial actions in real time have applications in a number of different fields. However, developing such a system is always a challeng...
We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data a...