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    » Arguing from Experience to Classifying Noisy Data
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    DATAMINE
    2002
    169views more  DATAMINE 2002»
    Advances in Instance Selection for Instance-Based Learning Algorithms
    13 years 7 months ago
    Advances in Instance Selection for Instance-Based Learning Algorithms
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    The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time ...
    Henry Brighton, Chris Mellish
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    AAAI
    2007
    103views Intelligent Agents» more  AAAI 2007»
    The Impact of Time on the Accuracy of Sentiment Classifiers Created from a Web Log Corpus
    13 years 9 months ago
    The Impact of Time on the Accuracy of Sentiment Classifiers Created from a Web Log Corpus
    Download  www.aaai.org
    We investigate the impact of time on the predictability of sentiment classification research for models created from web logs. We show that sentiment classifiers are time dependen...
    Kathleen T. Durant, Michael D. Smith
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    GRC
    2008
    IEEE
    173views Applied Computing» more  GRC 2008»
    Neighborhood Smoothing Embedding for Noisy Manifold Learning
    13 years 8 months ago
    Neighborhood Smoothing Embedding for Noisy Manifold Learning
    Download  www.cse.wustl.edu
    Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
    Guisheng Chen, Junsong Yin, Deyi Li
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    IJON
    2006
    85views more  IJON 2006»
    From outliers to prototypes: Ordering data
    13 years 7 months ago
    From outliers to prototypes: Ordering data
    Download  www.kyb.tuebingen.mpg.de
    We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show ...
    Stefan Harmeling, Guido Dornhege, David M. J. Tax,...
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    ISMB
    1993
    116views Computational Biology» more  ISMB 1993»
    Knowledge-Based Generation of Machine-Learning Experiments: Learning with DNA Crystallography Data
    13 years 8 months ago
    Knowledge-Based Generation of Machine-Learning Experiments: Learning with DNA Crystallography Data
    Download  www.aaai.org
    Thoughit has been possible in the past to learn to predict DNAhydration patterns from crystallographic data, there is ambiguity in the choice of training data (both in terms of th...
    Dawn M. Cohen, Casimir A. Kulikowski, Helen Berman
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