In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates ...
Davide Aliprandi, Alex Mancastroppa, Matteo Matteu...
While CSCW research has mostly been focusing on desktop applications there is a growing interest on ubiquitous and tangible computing. We present ethnographic fieldwork and prototy...
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 an ant-based colony system for the representation of a verbal route description. It is grounded on a natural metaphor that mimics the behavior of ant colonie...
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...