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Record Nr. |
UNISA996465540803316 |
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Titolo |
Multiple Classifier Systems [[electronic resource] ] : Third International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002. Proceedings / / edited by Fabio Roli, Josef Kittler |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 |
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ISBN |
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Edizione |
[1st ed. 2002.] |
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Descrizione fisica |
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1 online resource (X, 342 p.) |
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Collana |
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Lecture Notes in Computer Science, , 0302-9743 ; ; 2364 |
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Disciplina |
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Soggetti |
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Ensemble learning (Machine learning) |
Computer engineering |
Artificial intelligence |
Pattern recognition |
Optical data processing |
Algorithms |
Computer Engineering |
Artificial Intelligence |
Pattern Recognition |
Image Processing and Computer Vision |
Algorithm Analysis and Problem Complexity |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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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 -- |
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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. |
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