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    SIGKDD
    2008
    150views more  SIGKDD 2008»
    Learning to improve area-under-FROC for imbalanced medical data classification using an ensemble method
    13 years 8 months ago
    Learning to improve area-under-FROC for imbalanced medical data classification using an ensemble method
    Download  www.sigkdd.org
    This paper presents our solution for KDD Cup 2008 competition that aims at optimizing the area under ROC for breast cancer detection. We exploited weighted-based classification me...
    Hung-Yi Lo, Chun-Min Chang, Tsung-Hsien Chiang, Ch...
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    ML
    2002
    ACM
    220views Machine Learning» more  ML 2002»
    Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
    13 years 8 months ago
    Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
    Download  www.mth.kcl.ac.uk
    I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
    Peter Sollich
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    SAC
    2006
    ACM
    85views Applied Computing» more  SAC 2006»
    Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
    14 years 2 months ago
    Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
    Download  cimic.rutgers.edu
    Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security co...
    Hwanjo Yu, Xiaoqian Jiang, Jaideep Vaidya
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    TCBB
    2008
    138views more  TCBB 2008»
    PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM
    13 years 8 months ago
    PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM
    Download  www.stat.lsa.umich.edu
    The subcellular locations of proteins are important functional annotations. An effective and reliable subcellular localization method is necessary for proteomics research. This pap...
    Man-Wai Mak, Jian Guo, Sun-Yuan Kung
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    ICASSP
    2009
    IEEE
    134views Signal Processing» more  ICASSP 2009»
    High-level feature extraction using SVM with walk-based graph kernel
    14 years 20 days ago
    High-level feature extraction using SVM with walk-based graph kernel
    Download  cbio.ensmp.fr
    We investigate a method using support vector machines (SVMs) with walk-based graph kernels for high-level feature extraction from images. In this method, each image is first segme...
    Jean-Philippe Vert, Tomoko Matsui, Shin'ichi Satoh...
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