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» A New Framework for Dissimilarity and Similarity Learning
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ICML
2007
IEEE
16 years 6 months ago
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
A new hierarchical nonparametric Bayesian model is proposed for the problem of multitask learning (MTL) with sequential data. Sequential data are typically modeled with a hidden M...
Kai Ni, Lawrence Carin, David B. Dunson
AMDO
2006
Springer
15 years 9 months ago
Human Motion Synthesis by Motion Manifold Learning and Motion Primitive Segmentation
Abstract. We propose motion manifold learning and motion primitive segmentation framework for human motion synthesis from motion-captured data. High dimensional motion capture date...
Chan-Su Lee, Ahmed M. Elgammal
BMCBI
2007
133views more  BMCBI 2007»
15 years 6 months ago
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. ...
Ivan G. Costa, Roland Krause, Lennart Opitz, Alexa...
ISBI
2007
IEEE
16 years 10 days ago
Shape Analysis Using Curvature-Based Descriptors and Profile Hidden Markov Models
This paper presents a new framework for shape modeling and analysis. A shape instance is described by a curvature-based shape descriptor. A Profile Hidden Markov Model (PHMM) is ...
Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
ICRA
2009
IEEE
125views Robotics» more  ICRA 2009»
16 years 21 days ago
Learning motor primitives for robotics
— The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the...
Jens Kober, Jan Peters