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    » Learning of Boolean Functions Using Support Vector Machines
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    VLSID
    2005
    IEEE
    105views VLSI» more  VLSID 2005»
    Placement and Routing for 3D-FPGAs Using Reinforcement Learning and Support Vector Machines
    14 years 1 months ago
    Placement and Routing for 3D-FPGAs Using Reinforcement Learning and Support Vector Machines
    Download  www.cse.psu.edu
    The primary advantage of using 3D-FPGA over 2D-FPGA is that the vertical stacking of active layers reduce the Manhattan distance between the components in 3D-FPGA than when placed...
    R. Manimegalai, E. Siva Soumya, V. Muralidharan, B...
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    KDD
    2004
    ACM
    139views Data Mining» more  KDD 2004»
    Learning a complex metabolomic dataset using random forests and support vector machines
    14 years 8 months ago
    Learning a complex metabolomic dataset using random forests and support vector machines
    Download  math.uc.edu
    Metabolomics is the omics science of biochemistry. The associated data include the quantitative measurements of all small molecule metabolites in a biological sample. These datase...
    Young Truong, Xiaodong Lin, Chris Beecher
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    ESANN
    2007
    103views Neural Networks» more  ESANN 2007»
    Interval discriminant analysis using support vector machines
    13 years 9 months ago
    Interval discriminant analysis using support vector machines
    Download  www.dice.ucl.ac.be
    Imprecision, incompleteness, prior knowledge or improved learning speed can motivate interval–represented data. Most approaches for SVM learning of interval data use local kernel...
    Cecilio Angulo, Davide Anguita, Luis Gonzál...
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    BMCBI
    2008
    141views more  BMCBI 2008»
    Functional discrimination of membrane proteins using machine learning techniques
    13 years 7 months ago
    Functional discrimination of membrane proteins using machine learning techniques
    Download  www.biomedcentral.com
    Background: Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amin...
    M. Michael Gromiha, Yukimitsu Yabuki
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    BIOINFORMATICS
    2006
    105views more  BIOINFORMATICS 2006»
    Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure
    13 years 7 months ago
    Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure
    Download  noble.gs.washington.edu
    Darrin P. Lewis, Tony Jebara, William Stafford Nob...
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