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    » Using Supervised Clustering to Enhance Classifiers
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    WWW
    2004
    ACM
    156views Internet Technology» more  WWW 2004»
    Web taxonomy integration using support vector machines
    14 years 8 months ago
    Web taxonomy integration using support vector machines
    Download  www.iw3c2.org
    We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the eme...
    Dell Zhang, Wee Sun Lee
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    BMCBI
    2010
    118views more  BMCBI 2010»
    From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
    13 years 7 months ago
    From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
    Download  www.biomedcentral.com
    Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
    Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
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    SDM
    2010
    SIAM
    156views Data Mining» more  SDM 2010»
    Unsupervised Discovery of Abnormal Activity Occurrences in Multi-dimensional Time Series, with Applications in Wearable Systems
    13 years 8 months ago
    Unsupervised Discovery of Abnormal Activity Occurrences in Multi-dimensional Time Series, with Applications in Wearable Systems
    Download  cs.ucla.edu
    We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data. Unsupervised activity discovery approaches differ from ...
    Alireza Vahdatpour, Majid Sarrafzadeh
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    ICDM
    2009
    IEEE
    137views Data Mining» more  ICDM 2009»
    Regression Learning Vector Quantization
    14 years 2 months ago
    Regression Learning Vector Quantization
    Download  www.ist.temple.edu
    — Learning Vector Quantization (LVQ) is a popular class of nearest prototype classifiers for multiclass classification. Learning algorithms from this family are widely used becau...
    Mihajlo Grbovic, Slobodan Vucetic
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    BMCBI
    2005
    161views more  BMCBI 2005»
    Non-linear mapping for exploratory data analysis in functional genomics
    13 years 7 months ago
    Non-linear mapping for exploratory data analysis in functional genomics
    Download  www.biomedcentral.com
    Background: Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory...
    Francisco Azuaje, Haiying Wang, Alban Chesneau
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