Multiple Classifier Systems

12th International Workshop, MCS 2015, Günzburg, Germany, June 29 - July 1, 2015, Proceedings

Häftad, Engelska, 2015

Av Friedhelm Schwenker, Fabio Roli, Josef Kittler

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This book constitutes the refereed proceedings of the 12th International Workshop on Multiple Classifier Systems, MCS 2015, held in Günzburg, Germany, in June/July 2015. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics.

Produktinformation

Utgivningsdatum2015-06-12
Mått155 x 235 x 17 mm
Vikt376 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor231
FörlagSpringer International Publishing AG
ISBN9783319202471

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Systemvetenskap och AI inom Data och it
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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.