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    ICML
    2007
    IEEE
    106views Machine Learning» more  ICML 2007»
    Linear and nonlinear generative probabilistic class models for shape contours
    14 years 8 months ago
    Linear and nonlinear generative probabilistic class models for shape contours
    Download  www.machinelearning.org
    We introduce a robust probabilistic approach to modeling shape contours based on a lowdimensional, nonlinear latent variable model. In contrast to existing techniques that use obj...
    Graham McNeill, Sethu Vijayakumar
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    BC
    2002
    91views more  BC 2002»
    Linear combinations of nonlinear models for predicting human-machine interface forces
    13 years 7 months ago
    Linear combinations of nonlinear models for predicting human-machine interface forces
    Download  www.smpp.northwestern.edu
    ACT This study presents a computational framework that capitalizes on known human neuromechanical characteristics during limb movements in order to predict man-machine interactions...
    James L. Patton, Ferdinando A. Mussa-Ivaldi
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    BIOCOMP
    2006
    233views Bioinformatics» more  BIOCOMP 2006»
    Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene
    13 years 9 months ago
    Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene
    Download  ww1.ucmss.com
    Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectatio...
    Yu Zhang, Zhidong Deng, Hongshan Jiang, Peifa Jia
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    ICCV
    2011
    IEEE
    331views Computer Vision» more  ICCV 2011»
    Dynamic Manifold Warping for View Invariant Action Recognition
    12 years 7 months ago
    Dynamic Manifold Warping for View Invariant Action Recognition
    Download  www-scf.usc.edu
    We address the problem of learning view-invariant 3D models of human motion from motion capture data, in order to recognize human actions from a monocular video sequence with arbi...
    Dian Gong, Gerard Medioni
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    ICCV
    2009
    IEEE
    444views Computer Vision» more  ICCV 2009»
    Learning Pedestrian Dynamics from the Real World
    15 years 15 days ago
    Learning Pedestrian Dynamics from the Real World
    Download  www.cs.ucf.edu
    In this paper we describe a method to learn parameters which govern pedestrian motion by observing video data. Our learning framework is based on variational mode learning and a...
    Paul Scovanner, Marshall Tappen
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