00883cam0 22002893 450 E60020002864420241007083058.020070730d2004 |||||ita|0103 baitaIT<<Le >>ali spezzateGibran Kahlil Gibran3. edMilanoBUR2004111 p.18 cmBURI classici blu(ac)001LAEC000213622001 *BUR. I classici bluGibran, Kahlil <1883-1931>AF00006298070444293ITUNISOB20241007RICAUNISOBUNISOB892135556E600200028644M 102 Monografia moderna SBNM892000025SI135556acquistocatenacciUNISOBUNISOB20070730103207.020241007083058.0SpinosaAli spezzate1688928UNISOB05673nam 22008175 450 991014390140332120251202141006.03-540-45428-410.1007/3-540-45428-4(CKB)1000000000211768(SSID)ssj0000325035(PQKBManifestationID)11211312(PQKBTitleCode)TC0000325035(PQKBWorkID)10321026(PQKB)10821859(DE-He213)978-3-540-45428-1(MiAaPQ)EBC3073174(PPN)155208500(BIP)13638518(BIP)7818574(EXLCZ)99100000000021176820121227d2002 u| 0engurnn#008mamaatxtccrMultiple Classifier Systems Third International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002. Proceedings /edited by Fabio Roli, Josef Kittler1st ed. 2002.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2002.1 online resource (X, 342 p.)Lecture Notes in Computer Science,1611-3349 ;2364Bibliographic Level Mode of Issuance: Monograph3-540-43818-1 Includes bibliographical references at the end of each chapters and index.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.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.Lecture Notes in Computer Science,1611-3349 ;2364Computer engineeringComputer networksArtificial intelligencePattern recognition systemsComputer visionAlgorithmsComputer Engineering and NetworksArtificial IntelligenceAutomated Pattern RecognitionComputer VisionAlgorithmsComputer engineering.Computer networks.Artificial intelligence.Pattern recognition systems.Computer vision.Algorithms.Computer Engineering and Networks.Artificial Intelligence.Automated Pattern Recognition.Computer Vision.Algorithms.006.3/1Roli Fabioedthttp://id.loc.gov/vocabulary/relators/edtKittler Josefedthttp://id.loc.gov/vocabulary/relators/edtInternational Workshop on Multiple Classifier SystemsMiAaPQMiAaPQMiAaPQBOOK9910143901403321Multiple Classifier Systems772217UNINA