Background: Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interact...
Abstract: This paper presents a multiagent architecture and algorithms for collaborative, self-organizing learning in distributed, heterogeneous and dynamic business systems, where...
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
We present an extension of a computational cognitive model of social tagging and exploratory search called the semantic imitation model. The model assumes a probabilistic represen...
Wai-Tat Fu, Thomas George Kannampallil, Ruogu Kang
We present an algorithm that learns invariant features from real data in an entirely unsupervised fashion. The principal benefit of our method is that it can be applied without hu...