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    » Optimal feature selection for support vector machines
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    KDD
    2010
    ACM
    310views Data Mining» more  KDD 2010»
    An integrated machine learning approach to stroke prediction
    13 years 11 months ago
    An integrated machine learning approach to stroke prediction
    Download  ai.stanford.edu
    Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Accurate prediction of stroke is highly valuable for early...
    Aditya Khosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu-Ku...
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    CEC
    2007
    IEEE
    195views Artificial Intelligence» more  CEC 2007»
    Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms
    13 years 11 months ago
    Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms
    Download  www2.lifl.fr
    In this work we compare the use of a Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA) (both augmented with Support Vector Machines SVM) for the classification of high...
    Enrique Alba, José García-Nieto, Lae...
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    BMCBI
    2007
    153views more  BMCBI 2007»
    Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis
    13 years 7 months ago
    Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis
    Download  www.biomedcentral.com
    Background: A nanopore detector has a nanometer-scale trans-membrane channel across which a potential difference is established, resulting in an ionic current through the channel ...
    Matthew Landry, Stephen Winters-Hilt
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    TNN
    2010
    143views Management» more  TNN 2010»
    Using unsupervised analysis to constrain generalization bounds for support vector classifiers
    13 years 2 months ago
    Using unsupervised analysis to constrain generalization bounds for support vector classifiers
    Download  www.smartlab.dibe.unige.it
    Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
    Sergio Decherchi, Sandro Ridella, Rodolfo Zunino, ...
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    WCE
    2007
    180views Electrical And Computer Engi...» more  WCE 2007»
    Gene Selection for Tumor Classification Using Microarray Gene Expression Data
    13 years 9 months ago
    Gene Selection for Tumor Classification Using Microarray Gene Expression Data
    Download  www.iaeng.org
    – In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
    Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...
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