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    CIDM
    2009
    IEEE
    218views Artificial Intelligence» more  CIDM 2009»
    An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented software
    14 years 4 months ago
    An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented software
    Download  faculty.kfupm.edu.sa
    —  Identifying faulty classes in object-oriented software is one of the important software quality assurance activities. This paper empirically investigates the application of t...
    Hamoud I. Aljamaan, Mahmoud O. Elish
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    AAAI
    2004
    206views Intelligent Agents» more  AAAI 2004»
    Bayesian Network Classifiers Versus k-NN Classifier Using Sequential Feature Selection
    13 years 11 months ago
    Bayesian Network Classifiers Versus k-NN Classifier Using Sequential Feature Selection
    Download  www.aaai.org
    The aim of this paper is to compare Bayesian network classifiers to the k-NN classifier based on a subset of features. This subset is established by means of sequential feature se...
    Franz Pernkopf
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    ECCV
    2006
    Springer
    201views Computer Vision» more  ECCV 2006»
    Robust Face Alignment Based on Hierarchical Classifier Network
    13 years 11 months ago
    Robust Face Alignment Based on Hierarchical Classifier Network
    Download  iris.usc.edu
    Abstract. Robust face alignment is crucial for many face processing applications. As face detection only gives a rough estimation of face region, one important problem is how to al...
    Li Zhang, Haizhou Ai, Shihong Lao
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    IJIS
    2006
    115views more  IJIS 2006»
    Noisy-or classifier
    13 years 9 months ago
    Noisy-or classifier
    Download  staff.utia.cas.cz
    We discuss an application of a family of Bayesian network models
    Jirí Vomlel
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    ICML
    1999
    IEEE
    210views Machine Learning» more  ICML 1999»
    Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
    14 years 10 months ago
    Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
    Download  www.csse.monash.edu.au
    Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
    Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
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