Hierarchical classifier

Results: 19



#Item
1On-line Hierarchical Multi-label Text Classification Jesse Read September 7, 2007  On-line Hierarchical Multi-label Text Classification

On-line Hierarchical Multi-label Text Classification Jesse Read September 7, 2007 On-line Hierarchical Multi-label Text Classification

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Source URL: users.ics.aalto.fi

Language: English - Date: 2010-10-25 05:10:53
2Deep Classifier for Large Scale Hierarchical Text Classification Dingquan Wang, Weinan Zhang, Gui-Rong Xue, and Yong Yu Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Dongchuan Road. 800, 20024

Deep Classifier for Large Scale Hierarchical Text Classification Dingquan Wang, Weinan Zhang, Gui-Rong Xue, and Yong Yu Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Dongchuan Road. 800, 20024

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Source URL: www0.cs.ucl.ac.uk

Language: English - Date: 2012-10-22 20:10:23
    3Predicting Library of Congress Classifications From Library of Congress Subject Headings Eibe Frank Department of Computer Science University of Waikato

    Predicting Library of Congress Classifications From Library of Congress Subject Headings Eibe Frank Department of Computer Science University of Waikato

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    Source URL: www.cs.waikato.ac.nz

    Language: English - Date: 2004-07-25 19:48:49
    4Recursive Regularization for Large-scale Classification with Hierarchical and Graphical Dependencies∗ Siddharth Gopal Yiming Yang

    Recursive Regularization for Large-scale Classification with Hierarchical and Graphical Dependencies∗ Siddharth Gopal Yiming Yang

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    Source URL: nyc.lti.cs.cmu.edu

    Language: English - Date: 2013-09-29 12:55:31
    5Mixed-effects inference for classification studies Release v1.04 (r19160) for MATLAB, March 2013 Introduction Classification algorithms are often used in a hierarchical setting, where a classifier is trained and tested o

    Mixed-effects inference for classification studies Release v1.04 (r19160) for MATLAB, March 2013 Introduction Classification algorithms are often used in a hierarchical setting, where a classifier is trained and tested o

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    Source URL: people.inf.ethz.ch

    Language: English - Date: 2015-01-04 06:15:14
    6Mixed-effects inference for classification studies R package v1.02 (r19176), March 2013 Introduction Classification algorithms are often used in a hierarchical setting, where a classifier is trained and tested on individ

    Mixed-effects inference for classification studies R package v1.02 (r19176), March 2013 Introduction Classification algorithms are often used in a hierarchical setting, where a classifier is trained and tested on individ

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    Source URL: people.inf.ethz.ch

    Language: English - Date: 2015-01-04 06:15:15
    7Efficient probabilistic models for inference and learning Individual Grant Review Report EPSRC grant GR/N07394 Peter A. Flach Department of Computer Science, University of Bristol

    Efficient probabilistic models for inference and learning Individual Grant Review Report EPSRC grant GR/N07394 Peter A. Flach Department of Computer Science, University of Bristol

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    Source URL: www.cs.bris.ac.uk

    Language: English - Date: 2014-03-11 07:10:48
    8Refined Experts Improving Classification in Large Taxonomies Paul N. Bennett Microsoft Research One Microsoft Way Redmond, WA

    Refined Experts Improving Classification in Large Taxonomies Paul N. Bennett Microsoft Research One Microsoft Way Redmond, WA

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    Source URL: research.microsoft.com

    Language: English - Date: 2009-07-08 19:55:20
    9Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions. Devis Tuiaa , R´emi Flamaryb , Nicolas Courtyc b Laboratoire  a Department of Geography, University of Zurich, Switz

    Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions. Devis Tuiaa , R´emi Flamaryb , Nicolas Courtyc b Laboratoire a Department of Geography, University of Zurich, Switz

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    Source URL: remi.flamary.com

    Language: English - Date: 2015-01-14 03:20:20
    10¨ t Augsburg Universita Active Improvement of Hierarchical Object Features under Budget Constraints

    ¨ t Augsburg Universita Active Improvement of Hierarchical Object Features under Budget Constraints

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    Source URL: www.multimedia-computing.de

    Language: English - Date: 2011-06-17 18:08:22