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    NIPS
    2001
    142views Information Technology» more  NIPS 2001»
    On the Generalization Ability of On-Line Learning Algorithms
    13 years 9 months ago
    On the Generalization Ability of On-Line Learning Algorithms
    Download  homes.dsi.unimi.it
    In this paper, it is shown how to extract a hypothesis with small risk from the ensemble of hypotheses generated by an arbitrary on-line learning algorithm run on an independent an...
    Nicolò Cesa-Bianchi, Alex Conconi, Claudio ...
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    ENC
    2006
    IEEE
    122views Theoretical Computer Science» more  ENC 2006»
    Cleaning Training-Datasets with Noise-Aware Algorithms
    14 years 1 months ago
    Cleaning Training-Datasets with Noise-Aware Algorithms
    Download  ccc.inaoep.mx
    We introduce a novel learning algorithm for noise elimination. Our algorithm is based on the re-measurement idea for the correction of erroneous observations and is able to discri...
    H. Jair Escalante
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    FLAIRS
    2006
    86views Artificial Intelligence» more  FLAIRS 2006»
    Analysis of Galactic Spectra using Noise-Aware Learning Algorithms
    13 years 9 months ago
    Analysis of Galactic Spectra using Noise-Aware Learning Algorithms
    Download  www.aaai.org
    We introduce a novel learning algorithm for noise elimination. Our algorithm is based on the re-measurement idea for the correction of erroneous observations and is able to discri...
    H. Jair Escalante, Olac Fuentes
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    EOR
    2007
    165views more  EOR 2007»
    Adaptive credit scoring with kernel learning methods
    13 years 7 months ago
    Adaptive credit scoring with kernel learning methods
    Download  www.crc.man.ed.ac.uk
    Credit scoring is a method of modelling potential risk of credit applications. Traditionally, logistic regression, linear regression and discriminant analysis are the most popular...
    Yingxu Yang
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    KDD
    2012
    ACM
    187views Data Mining» more  KDD 2012»
    Online learning to diversify from implicit feedback
    11 years 10 months ago
    Online learning to diversify from implicit feedback
    Download  www.cs.cornell.edu
    In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
    Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
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