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    KDD
    1995
    ACM
    109views Data Mining» more  KDD 1995»
    An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers
    14 years 1 months ago
    An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers
    Download  www.aaai.org
    The Bayesianclassifier is a simple approachto classification that producesresults that are easy for people to interpret. In many cases, the Bayesianclassifieris at leastasaccurate...
    Michael J. Pazzani
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    ICIC
    2009
    Springer
    217views Applied Computing» more  ICIC 2009»
    Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification
    14 years 2 months ago
    Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification
    Download  polyu.academia.edu
    Plant has plenty use in foodstuff, medicine and industry, and is also vitally important for environmental protection. So, it is important and urgent to recognize and classify plant...
    Shanwen Zhang, Kwok-Wing Chau
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    IEEEICCI
    2009
    IEEE
    309views Artificial Intelligence» more  IEEEICCI 2009»
    Classifier ensemble based analysis of a genome-wide SNP dataset concerning Late-Onset Alzheimer Disease
    14 years 4 months ago
    Classifier ensemble based analysis of a genome-wide SNP dataset concerning Late-Onset Alzheimer Disease
    Download  biomind.com
    The OpenBiomind toolkit is used to apply GA, GP and local search methods to analyze a large SNP dataset concerning late-onset Alzheimers disease (LOAD). Classification models iden...
    Lúcio de Souza Coelho, Ben Goertzel, Cassio...
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    EPIA
    2009
    Springer
    158views Artificial Intelligence» more  EPIA 2009»
    Classifying Documents According to Locational Relevance
    14 years 4 months ago
    Classifying Documents According to Locational Relevance
    Download  xldb.fc.ul.pt
    This paper presents an approach for categorizing documents according to their implicit locational relevance. We report a thorough evaluation of several classifiers designed for th...
    Ivo Anastácio, Bruno Martins, Pável ...
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    ESANN
    2007
    134views Neural Networks» more  ESANN 2007»
    Classifying n-back EEG data using entropy and mutual information features
    13 years 11 months ago
    Classifying n-back EEG data using entropy and mutual information features
    Download  www.physics.brown.edu
    In this work we show that entropy (H) and mutual information (MI) can be used as methods for extracting spatially localized features for classification purposes. In order to incre...
    Liang Wu, Predrag Neskovic, Etienne Reyes, Elena F...
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