Tech Reports
Tech Report kmi-99-03 Abstract
Bayesian Clustering of Sensory Inputs by Dynamics
Techreport ID: kmi-99-03
Date: 1999
Author(s): Paola Sebastiani, Marco Ramoni and Paul Cohen
This paper introduces a Bayesian method for unsupervised clustering of dynamic processes and applies it to the abstraction of sensory inputs of a mobile robot. The method starts by transforming the sensory inputs into Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing the robot's experiences. To increase efficiency, the method uses an entropy-based heuristic search strategy. 1. Department of Statistics, The Open University. 2. Knowledge Media Institute, The Open University. 3. Department of Computer Science, University of Massachusetts at Amherst.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Knowledge Management is...

Our aim is to capture, analyse and organise knowledge, regardless of its origin and form and make it available to the learner when needed presented with the necessary context and in a form supporting the learning processes.
Check out these Hot Knowledge Management Projects:
List all Knowledge Management Projects
Check out these Hot Knowledge Management Technologies:
List all Knowledge Management Technologies
List all Knowledge Management Projects
Check out these Hot Knowledge Management Technologies:
List all Knowledge Management Technologies

