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    NIPS
    2004
    112views Information Technology» more  NIPS 2004»
    Learning first-order Markov models for control
    15 years 4 months ago
    Learning first-order Markov models for control
    Download  books.nips.cc
    First-order Markov models have been successfully applied to many problems, for example in modeling sequential data using Markov chains, and modeling control problems using the Mar...
    Pieter Abbeel, Andrew Y. Ng
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    109
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    NN
    2006
    Springer
    153views Neural Networks» more  NN 2006»
    An incremental network for on-line unsupervised classification and topology learning
    15 years 2 months ago
    An incremental network for on-line unsupervised classification and topology learning
    Download  haselab.info
    This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity thresholdbased and a local error-based insertion c...
    Shen Furao, Osamu Hasegawa
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    109
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    JAIR
    2010
    111views more  JAIR 2010»
    Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language
    15 years 1 months ago
    Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language
    Download  www.cs.utexas.edu
    We present a novel framework for learning to interpret and generate language using only perceptual context as supervision. We demonstrate its capabilities by developing a system t...
    David L. Chen, Joohyun Kim, Raymond J. Mooney
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    130
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    EMNLP
    2009
    141views Natural Language Processing» more  EMNLP 2009»
    Semi-Supervised Learning for Semantic Relation Classification using Stratified Sampling Strategy
    15 years 12 days ago
    Semi-Supervised Learning for Semantic Relation Classification using Stratified Sampling Strategy
    Download  www.aclweb.org
    This paper presents a new approach to selecting the initial seed set using stratified sampling strategy in bootstrapping-based semi-supervised learning for semantic relation class...
    Longhua Qian, Guodong Zhou, Fang Kong, Qiaoming Zh...
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    115
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    ICMLA
    2009
    169views Machine Learning» more  ICMLA 2009»
    Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
    15 years 10 days ago
    Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
    Download  www.ipb.uni-bonn.de
    Abstract--This paper presents a novel and domainindependent approach for graph-based structure learning. The approach is based on solving the Maximum Common SubgraphIsomorphism pro...
    Johannes Hartz
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