Multiple Classifier Systems Third International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002. Proceedings /
Multiple Classifier Systems Third International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002. Proceedings / [electronic resource] :
edited by Fabio Roli, Josef Kittler.
- 1st ed. 2002.
- X, 342 p. online resource.
- Lecture Notes in Computer Science, 2364 1611-3349 ; .
- Lecture Notes in Computer Science, 2364 .
Invited Papers -- Multiclassifier Systems: Back to the Future -- Support Vector Machines, Kernel Logistic Regression and Boosting -- Multiple Classification Systems in the Context of Feature Extraction and Selection -- Bagging and Boosting -- Boosted Tree Ensembles for Solving Multiclass Problems -- Distributed Pasting of Small Votes -- Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy -- Highlighting Hard Patterns via AdaBoost Weights Evolution -- Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse -- Ensemble Learning and Neural Networks -- Multistage Neural Network Ensembles -- Forward and Backward Selection in Regression Hybrid Network -- Types of Multinet System -- Discriminant Analysis and Factorial Multiple Splits in Recursive Partitioning for Data Mining -- Design Methodologies -- New Measure of Classifier Dependency in Multiple Classifier Systems -- A Discussion on the Classifier Projection Space for Classifier Combining -- On the General Application of the Tomographic Classifier Fusion Methodology -- Post-processing of Classifier Outputs in Multiple Classifier Systems -- Combination Strategies -- Trainable Multiple Classifier Schemes for Handwritten Character Recognition -- Generating Classifier Ensembles from Multiple Prototypes and Its Application to Handwriting Recognition -- Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data -- Stacking with Multi-response Model Trees -- On Combining One-Class Classifiers for Image Database Retrieval -- Analysis and Performance Evaluation -- Bias—Variance Analysis and Ensembles of SVM -- An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs -- Reduction of the Boasting Bias of Linear Experts.-Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers -- Applications -- Boosting and Classification of Electronic Nose Data -- Content-Based Classification of Digital Photos -- Classifier Combination for In Vivo Magnetic Resonance Spectra of Brain Tumours -- Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach -- A Multi-expert System for Movie Segmentation -- Decision Level Fusion of Intramodal Personal Identity Verification Experts -- An Experimental Comparison of Classifier Fusion Rules for Multimodal Personal Identity Verification Systems.
9783540454281
10.1007/3-540-45428-4 doi
Computer engineering.
Computer networks .
Artificial intelligence.
Pattern recognition systems.
Computer vision.
Algorithms.
Computer Engineering and Networks.
Artificial Intelligence.
Automated Pattern Recognition.
Computer Vision.
Algorithms.
TK7885-7895 TK5105.5-5105.9
621.39 004.6
Invited Papers -- Multiclassifier Systems: Back to the Future -- Support Vector Machines, Kernel Logistic Regression and Boosting -- Multiple Classification Systems in the Context of Feature Extraction and Selection -- Bagging and Boosting -- Boosted Tree Ensembles for Solving Multiclass Problems -- Distributed Pasting of Small Votes -- Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy -- Highlighting Hard Patterns via AdaBoost Weights Evolution -- Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse -- Ensemble Learning and Neural Networks -- Multistage Neural Network Ensembles -- Forward and Backward Selection in Regression Hybrid Network -- Types of Multinet System -- Discriminant Analysis and Factorial Multiple Splits in Recursive Partitioning for Data Mining -- Design Methodologies -- New Measure of Classifier Dependency in Multiple Classifier Systems -- A Discussion on the Classifier Projection Space for Classifier Combining -- On the General Application of the Tomographic Classifier Fusion Methodology -- Post-processing of Classifier Outputs in Multiple Classifier Systems -- Combination Strategies -- Trainable Multiple Classifier Schemes for Handwritten Character Recognition -- Generating Classifier Ensembles from Multiple Prototypes and Its Application to Handwriting Recognition -- Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data -- Stacking with Multi-response Model Trees -- On Combining One-Class Classifiers for Image Database Retrieval -- Analysis and Performance Evaluation -- Bias—Variance Analysis and Ensembles of SVM -- An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs -- Reduction of the Boasting Bias of Linear Experts.-Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers -- Applications -- Boosting and Classification of Electronic Nose Data -- Content-Based Classification of Digital Photos -- Classifier Combination for In Vivo Magnetic Resonance Spectra of Brain Tumours -- Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach -- A Multi-expert System for Movie Segmentation -- Decision Level Fusion of Intramodal Personal Identity Verification Experts -- An Experimental Comparison of Classifier Fusion Rules for Multimodal Personal Identity Verification Systems.
9783540454281
10.1007/3-540-45428-4 doi
Computer engineering.
Computer networks .
Artificial intelligence.
Pattern recognition systems.
Computer vision.
Algorithms.
Computer Engineering and Networks.
Artificial Intelligence.
Automated Pattern Recognition.
Computer Vision.
Algorithms.
TK7885-7895 TK5105.5-5105.9
621.39 004.6