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» A New Framework for Dissimilarity and Similarity Learning
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ICML
2007
IEEE
15 years 10 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 1 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»
14 years 9 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
15 years 3 months 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
77
Voted
ICRA
2009
IEEE
125views Robotics» more  ICRA 2009»
15 years 4 months 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