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    ESANN
    2008
    90views Neural Networks» more  ESANN 2008»
    Comparison of sparse least squares support vector regressors trained in primal and dual
    13 years 9 months ago
    Comparison of sparse least squares support vector regressors trained in primal and dual
    Download  www.dice.ucl.ac.be
    In our previous work, we have developed sparse least squares support vector regressors (sparse LS SVRs) trained in the primal form in the reduced empirical feature space. In this p...
    Shigeo Abe
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    ICPR
    2004
    IEEE
    158views computer vision» more  ICPR 2004»
    Face Detection Using Discriminating Feature Analysis and Support Vector Machine in Video
    14 years 9 months ago
    Face Detection Using Discriminating Feature Analysis and Support Vector Machine in Video
    Download  www.cs.njit.edu
    This paper presents a novel face detection method in video by using Discriminating Feature Analysis (DFA) and Support Vector Machine (SVM). Our method first incorporates temporal ...
    Chengjun Liu, Peichung Shih
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    NAACL
    2004
    112views Computational Linguistics» more  NAACL 2004»
    Shallow Semantic Parsing using Support Vector Machines
    13 years 9 months ago
    Shallow Semantic Parsing using Support Vector Machines
    Download  www.stanford.edu
    In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algor...
    Sameer Pradhan, Wayne Ward, Kadri Hacioglu, James ...
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    BMCBI
    2007
    139views more  BMCBI 2007»
    Improving model predictions for RNA interference activities that use support vector machine regression by combining and filterin
    13 years 8 months ago
    Improving model predictions for RNA interference activities that use support vector machine regression by combining and filterin
    Download  www.biomedcentral.com
    Background: RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathwa...
    Andrew S. Peek
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    IJCNN
    2008
    IEEE
    139views Neural Networks» more  IJCNN 2008»
    Sparse support vector machines trained in the reduced empirical feature space
    14 years 2 months ago
    Sparse support vector machines trained in the reduced empirical feature space
    Download  www.lib.kobe-u.ac.jp
    — We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
    Kazuki Iwamura, Shigeo Abe
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