多分类器系统 Multiple classifier systems
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作者： Fabio Roli 著
出 版 社： 湖南文艺出版社
出版时间： 2002-12-1字数：版次： 1页数： 335印刷时间： 2002/12/01开本：印次：纸张： 胶版纸I S B N ： 9783540438182包装： 平装编辑推荐
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内容简介
This book constitutes the refereed proceedings of the Third International Workshop on Multiple Classifier Systems, MCS 2002, held in Cagliari, Italy, in June 2002.The 29 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on bagging and boosting, ensemble learning and neural networks, design methodologies, combination strategies, analysis and performance evaluation, and applications.
目录
Invited Papers
Multiclassifier Systems: Back to the ~ture
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 Classifiers Ensembles from Multiple Prototypes and Its Application to Handwriting Recognition
Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth
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 Rules for Crisp Classifiers Outputs
……
Applications
Author Index
