Multiple classifier systems = 12th I...
Schwenker, Friedhelm.

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  • Multiple classifier systems = 12th International Workshop, MCS 2015, Gunzburg, Germany, June 29 - July 1, 2015 : proceedings /
  • Record Type: Language materials, printed : Monograph/item
    Title/Author: Multiple classifier systems/ edited by Friedhelm Schwenker, Fabio Roli, Josef Kittler.
    Reminder of title: 12th International Workshop, MCS 2015, Gunzburg, Germany, June 29 - July 1, 2015 : proceedings /
    remainder title: MCS 2015
    other author: Schwenker, Friedhelm.
    Published: Cham :Springer International Publishing : : 2015.,
    Description: x, 231 p. :ill., digital ;24 cm.
    [NT 15003449]: A Novel Bagging Ensemble Approach for Variable Ranking and Selection for Linear Regression Models -- A Hierarchical Ensemble Method for DAG-Structured Taxonomies -- Diversity Measures and Margin Criteria in Multi-class Majority Vote Ensemble -- Fractional Programming Weighted Decoding for Error-Correcting Output Codes -- Instance-Based Decompositions of Error Correcting Output Codes -- Pruning Bagging Ensembles with Metalearning -- Multi-label Selective Ensemble -- Supervised Selective Combination of Diverse Object-Representation Modalities for Regression Estimation -- Detecting Ordinal Class Structures -- Calibrating AdaBoost for Asymmetric Learning -- Building Classifier Ensembles Using Greedy Graph Edit Distance -- Measuring the Stability of Feature Selection with Applications to Ensemble Methods -- Suboptimal Graph Edit Distance Based on Sorted Local Assignments -- Multimodal PLSA for Movie Genre Classification -- One-and-a-Half-Class Multiple Classifier Systems for Secure Learning Against Evasion Attacks at Test Time -- An Experimental Study on Combining Binarization Techniques and Ensemble Methods of Decision Trees -- Decision Tree-Based Multiple Classifier Systems: An FPGA Perspective -- An Empirical Investigation on the Use of Diversity for Creation of Classifier Ensembles -- Bio-Visual Fusion for Person Independent Recognition of Pain Intensity.
    Contained By: Springer eBooks
    Subject: Machine learning -
    Online resource: http://dx.doi.org/10.1007/978-3-319-20248-8
    ISBN: 9783319202488 (electronic bk.)
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