Multiple Classifier Systems

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

AvFriedhelm Schwenker,Fabio Roli

Häftad, Engelska, 2015

Del 9132 i serien Lecture Notes in Computer Science

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Beskrivning

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 19 revised papers presented were carefully reviewed and selected from 25 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics. They are organized in topical sections on theory and algorithms and application and evaluation.

Produktinformation

Utgivningsdatum:2015-06-12
Mått:155 x 235 x 17 mm
Vikt:376 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:231
Förlag:Springer International Publishing AG
ISBN:9783319202471

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT

Innehållsförteckning

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.