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    BMCBI
    2004
    133views more  BMCBI 2004»
    Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction m
    13 years 8 months ago
    Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction m
    Download  www.biomedcentral.com
    Background: Screening of various gene markers such as single nucleotide polymorphism (SNP) and correlation between these markers and development of multifactorial disease have pre...
    Yasuyuki Tomita, Shuta Tomida, Yuko Hasegawa, Yoic...
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    IJCNN
    2007
    IEEE
    131views Neural Networks» more  IJCNN 2007»
    Probability Density Function Estimation Using Orthogonal Forward Regression
    14 years 2 months ago
    Probability Density Function Estimation Using Orthogonal Forward Regression
    Download  users.ecs.soton.ac.uk
    — Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression tec...
    Sheng Chen, Xia Hong, Chris J. Harris
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    NN
    2006
    Springer
    163views Neural Networks» more  NN 2006»
    Machine learning approaches for estimation of prediction interval for the model output
    13 years 8 months ago
    Machine learning approaches for estimation of prediction interval for the model output
    Download  www.unesco-ihe.org
    A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the pr...
    Durga L. Shrestha, Dimitri P. Solomatine
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    ICANN
    2009
    Springer
    203views Neural Networks» more  ICANN 2009»
    Empirical Study of the Universum SVM Learning for High-Dimensional Data
    14 years 1 months ago
    Empirical Study of the Universum SVM Learning for High-Dimensional Data
    Download  www.ece.umn.edu
    Abstract. Many applications of machine learning involve sparse highdimensional data, where the number of input features is (much) larger than the number of data samples, d n. Predi...
    Vladimir Cherkassky, Wuyang Dai
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    Book
    519views
    Information Theory, Inference, and Learning Algorithms
    15 years 7 months ago
    Information Theory, Inference, and Learning Algorithms
    Download  www.inference.phy.cam.ac.uk
    This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and...
    David J. C. MacKay
    posted by scimaster
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