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ICPR
2006
IEEE
14 years 10 months ago
A New Data Selection Principle for Semi-Supervised Incremental Learning
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...
Alexander I. Rudnicky, Rong Zhang
IROS
2008
IEEE
135views Robotics» more  IROS 2008»
14 years 3 months ago
Interactive learning of visual topological navigation
— We present a topological navigation system that is able to visually recognize the different rooms of an apartment and guide a robot between them. Specifically tailored for sma...
David Filliat
ECAI
2004
Springer
14 years 2 months ago
Avatars That Learn How to Behave
It is possible to model avatars that learn to simulate object manipulations and other complex actions. A number of applications may benefit from this technique including safety, e...
Adam Szarowicz, Paolo Remagnino
BIS
2008
132views Business» more  BIS 2008»
13 years 10 months ago
Evaluate - An Innovative Service for Learning Performance Monitoring in Businesses
In this paper we present Evaluate, a platform for learning performance monitoring. Evaluate manages a number of artefacts that can be used to monitor learning performance, like met...
Bernd Simon, Kasra Seirafi, Asmund Realfsen, Mark ...
CISST
2004
164views Hardware» more  CISST 2004»
13 years 10 months ago
Probabilistic Region Relevance Learning for Content-Based Image Retrieval
Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
Iker Gondra, Douglas R. Heisterkamp