Multiple Classifier Systems 12th International Workshop, MCS 2015, G�unzburg, Germany, June 29 - July 1, 2015, Proceedings / [electronic resource] :
edited by Friedhelm Schwenker, Fabio Roli, Josef Kittler.
- X, 231 p. 40 illus. online resource.
- Lecture Notes in Computer Science, 9132 0302-9743 ; .
- Lecture Notes in Computer Science, 9132 .
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.
This book constitutes the refereed proceedings of the 12th International Workshop on Multiple Classifier Systems, MCS 2015, held in G�unzburg, 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.
9783319202488
10.1007/978-3-319-20248-8 doi
Computer science. Data mining. Information storage and retrieval. Image processing. Pattern recognition. Computer Science. Data Mining and Knowledge Discovery. Pattern Recognition. Image Processing and Computer Vision. Information Storage and Retrieval.