139 search results - page 19 / 28 » A Bayesian approach to empirical local linearization for rob... |
TKDE
2010
13 years 6 months ago
2010
—Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not complet...
NIPS
1998
13 years 9 months ago
1998
We present a probabilistic method for fusion of images produced by multiple sensors. The approach is based on an image formation model in which the sensor images are noisy, locall...
ICRA
2008
IEEE
14 years 2 months ago
2008
IEEE
Abstract— Learning inverse kinematics has long been fascinating the robot learning community. While humans acquire this transformation to complicated tool spaces with ease, it is...
UAI
2003
13 years 9 months ago
2003
In this paper we present a family of models and learning algorithms that can simultaneously align and cluster sets of multidimensional curves measured on a discrete time grid. Our...
SIGIR
2006
ACM
14 years 1 months ago
2006
ACM
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...