We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded b...
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
This paper describes a parameter estimation method for multi-label classification that does not rely on approximate inference. It is known that multi-label classification involvin...
In this paper we solve the problem of classifying chestnut plants according to their place of origin. We compare the results obtained by state of the art classifiers, among which,...
In this paper, we present a novel texture classification algorithm inspired by the self-assembling behavior of real ants when building live structures with their bodies. The propo...