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JMLR
2008
151views more  JMLR 2008»
13 years 9 months ago
Learning to Combine Motor Primitives Via Greedy Additive Regression
The computational complexities arising in motor control can be ameliorated through the use of a library of motor synergies. We present a new model, referred to as the Greedy Addit...
Manu Chhabra, Robert A. Jacobs
ML
1998
ACM
131views Machine Learning» more  ML 1998»
13 years 9 months ago
Learning from Examples and Membership Queries with Structured Determinations
It is well known that prior knowledge or bias can speed up learning, at least in theory. It has proved di cult to make constructive use of prior knowledge, so that approximately c...
Prasad Tadepalli, Stuart J. Russell
KDD
2005
ACM
149views Data Mining» more  KDD 2005»
14 years 2 months ago
A distributed learning framework for heterogeneous data sources
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Srujana Merugu, Joydeep Ghosh
BMCBI
2004
140views more  BMCBI 2004»
13 years 9 months ago
What can we learn from noncoding regions of similarity between genomes?
Background: In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to as...
Thomas A. Down, Tim J. P. Hubbard
WAPCV
2007
Springer
14 years 3 months ago
Embodied Active Vision in Language Learning and Grounding
Abstract. Most cognitive studies of language acquisition in both natural systems and artificial systems have focused on the role of purely linguistic information as the central co...
Chen Yu