多分类器系统Multiple classifier systems( 多级分类器系统 )
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作者： Josef Kittler 著
出 版 社： 湖南文艺出版社
出版时间： 2001-12-1字数：版次： 1页数： 456印刷时间： 2006/12/01开本：印次：纸张： 胶版纸I S B N ： 9783540422846包装： 平装编辑推荐
The LNCS series reports state-of-the-art results in computer science research，development，and education，at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community，with numerous individuals，as well as with prestigious organizations and societies，LNCS has grown into the most comprehensive computer science research forum available.
The scope of LNCS，including its subseries LNAI，spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes.
—proceedings （published in time for the respective conference）
—post-proceedings （consisting of thoroughly revised final full papers）
—research monographs（which may be based on outstanding PhD work，research projects，technical reports，etc.）.
内容简介
This book constitutes the refereed proceedings of the Second International Workshop on Multiple Classifier Systems, MCS 2001, held in Cambridge, UK in July 2001.The 44 revised papers presented were carefully reviewed and selected for presentation. The book offers topical sections on bagging and boosting, MCS design methodology, ensemble classifiers, feature spaces for MCS, MCS in remote sensing, one class MCS and clustering, and combination strategies.
Proceedings of the Second Intl Workshop on Multiple Classifier Systems, MCS 2001, held in Cambridge, UK, in July 2001. Softcover.
目录
Bagging and Boosting
Bagging and the Random Subspace Method for Redundant Feature Spaces
Performance Degradation in Boosting
A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models
Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis
Learning Classification RBF Networks by Boosting
MCS Design Methodology
Data Complexity Analysis for Classifier Combination
Genetic Programming for Improved Receiver Operating Characteristics
Methods for Designing Multiple Classifier Systems
Decision-Level Fusion in Fingerprint Verification
Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition
Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method' .
Averaging Weak Classifiers
Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds
Ensemble Classifiers
Multiple Classifier Systems Based on Interpretable Linear Classifiers
Least Squares and Estimation Measures via Error Correcting Output Code
Dependence among Codeword Bits Errors in ECOC Learning Machines An Experimental Analysis
Information Analysis of Multiple Classifier Fusion
Limiting the Number of Trees in Random Forests
Learning-Data Selection Mechanism through Neural Networks Ensemble
A Multi-SVM Classification System
Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System
Feature Spaces for MCS
Feature Weighted Ensemble Classifiers - A Modified Decision Scheme
Feature Subsets for Classifier Combination: An Enumerative Experiment
Input Decimation Ensembles: Decorrelating through Dimensionality Reduction
Classifier Combination as a Tomographic Process
MCS in Remote Sensing
One Class MCS and Clustering
Combination Strategies
Author Index
