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    BIBE
    2009
    IEEE
    131views Bioinformatics» more  BIBE 2009»
    Learning Scaling Coefficient in Possibilistic Latent Variable Algorithm from Complex Diagnosis Data
    15 years 10 months ago
    Learning Scaling Coefficient in Possibilistic Latent Variable Algorithm from Complex Diagnosis Data
    Download  eshare.stut.edu.tw
    —The Possibilistic Latent Variable (PLV) clustering algorithm is a powerful tool for the analysis of complex datasets due to its robustness toward data distributions of different...
    Zong-Xian Yin
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    JMLR
    2012
    183views Programming Languages» more  JMLR 2012»
    Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets
    13 years 9 months ago
    Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets
    Download  jmlr.csail.mit.edu
    We propose a new method for comparing learning algorithms on multiple tasks which is based on a novel non-parametric test that we call the Poisson binomial test. The key aspect of...
    Alexandre Lacoste, François Laviolette, Mar...
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    STOC
    1996
    ACM
    104views Algorithms» more  STOC 1996»
    On the Boosting Ability of Top-Down Decision Tree Learning Algorithms
    15 years 11 months ago
    On the Boosting Ability of Top-Down Decision Tree Learning Algorithms
    Download  www.cis.upenn.edu
    Michael J. Kearns, Yishay Mansour
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    INFORMATICALT
    2006
    88views more  INFORMATICALT 2006»
    Improving the Performances of Asynchronous Algorithms by Combining the Nogood Processors with the Nogood Learning Techniques
    15 years 6 months ago
    Improving the Performances of Asynchronous Algorithms by Combining the Nogood Processors with the Nogood Learning Techniques
    Download  www.mii.lt
    Abstract. The asynchronous techniques that exist within the programming with distributed constraints are characterized by the occurrence of the nogood values during the search for ...
    Ionel Muscalagiu, Vladimir Cretu
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    ACL
    2008
    123views Computational Linguistics» more  ACL 2008»
    Semi-Supervised Convex Training for Dependency Parsing
    15 years 8 months ago
    Semi-Supervised Convex Training for Dependency Parsing
    Download  www.aclweb.org
    We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a discr...
    Qin Iris Wang, Dale Schuurmans, Dekang Lin
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