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    NIPS
    1998
    156views Information Technology» more  NIPS 1998»
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    13 years 9 months ago
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    Download  www.cs.nyu.edu
    The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
    Zoubin Ghahramani, Sam T. Roweis
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    JKM
    2006
    135views more  JKM 2006»
    Learning from the Mars Rover Mission: scientific discovery, learning and memory
    13 years 8 months ago
    Learning from the Mars Rover Mission: scientific discovery, learning and memory
    Download  ti.arc.nasa.gov
    Purpose Knowledge management for space exploration is part of a multi-generational effort. Each mission builds on knowledge from prior missions, and learning is the first step in ...
    Charlotte Linde
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    PR
    2007
    146views more  PR 2007»
    ML-KNN: A lazy learning approach to multi-label learning
    13 years 7 months ago
    ML-KNN: A lazy learning approach to multi-label learning
    Download  cse.seu.edu.cn
    Abstract: Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In mul...
    Min-Ling Zhang, Zhi-Hua Zhou
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    CN
    2007
    149views more  CN 2007»
    Adaptive congestion protocol: A congestion control protocol with learning capability
    13 years 8 months ago
    Adaptive congestion protocol: A congestion control protocol with learning capability
    Download  www.cs.ucy.ac.cy
    There is strong evidence that the current implementation of TCP will perform poorly in future high speed networks. To address this problem many congestion control protocols have b...
    Marios Lestas, Andreas Pitsillides, Petros A. Ioan...
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    BMCBI
    2010
    118views more  BMCBI 2010»
    From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
    13 years 8 months ago
    From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
    Download  www.biomedcentral.com
    Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
    Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
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