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Multiple Classifier Systems [electronic resource] :First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings.

Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 1857Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2000.Description: XII, 408 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540450146.Subject(s): Computer science | Computers | Algorithms | Artificial intelligence | Image processing | Pattern recognition | Computer Science | Pattern Recognition | Artificial Intelligence (incl. Robotics) | Image Processing and Computer Vision | Algorithm Analysis and Problem Complexity | Computation by Abstract DevicesOnline resources: Click here to access online
Contents:
Ensemble Methods in Machine Learning -- Experiments with Classifier Combining Rules -- The “Test and Select” Approach to Ensemble Combination -- A Survey of Sequential Combination of Word Recognizers in Handwritten Phrase Recognition at CEDAR -- Multiple Classifier Combination Methodologies for Different Output Levels -- A Mathematically Rigorous Foundation for Supervised Learning -- Classifier Combinations: Implementations and Theoretical Issues -- Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification -- Complexity of Classification Problems and Comparative Advantages of Combined Classifiers -- Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems -- Combining Fisher Linear Discriminants for Dissimilarity Representations -- A Learning Method of Feature Selection for Rough Classification -- Analysis of a Fusion Method for Combining Marginal Classifiers -- A hybrid projection based and radial basis function architecture -- Combining Multiple Classifiers in Probabilistic Neural Networks -- Supervised Classifier Combination through Generalized Additive Multi-model -- Dynamic Classifier Selection -- Boosting in Linear Discriminant Analysis -- Different Ways of Weakening Decision Trees and Their Impact on Classification Accuracy of DT Combination -- Applying Boosting to Similarity Literals for Time Series Classification -- Boosting of Tree-Based Classifiers for Predictive Risk Modeling in GIS -- A New Evaluation Method for Expert Combination in Multi-expert System Designing -- Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems -- Self-Organizing Decomposition of Functions -- Classifier Instability and Partitioning -- A Hierarchical Multiclassifier System for Hyperspectral Data Analysis -- Consensus Based Classification of Multisource Remote Sensing Data -- Combining Parametric and Nonparametric Classifiers for an Unsupervised Updating of Land-Cover Maps -- A Multiple Self-Organizing Map Scheme for Remote Sensing Classification -- Use of Lexicon Density in Evaluating Word Recognizers -- A Multi-expert System for Dynamic Signature Verification -- A Cascaded Multiple Expert System for Verification -- Architecture for Classifier Combination Using Entropy Measures -- Combining Fingerprint Classifiers -- Statistical Sensor Calibration for Fusion of Different Classifiers in a Biometric Person Recognition Framework -- A Modular Neuro-Fuzzy Network for Musical Instruments Classification -- Classifier Combination for Grammar-Guided Sentence Recognition -- Shape Matching and Extraction by an Array of Figure-and-Ground Classifiers.
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Ensemble Methods in Machine Learning -- Experiments with Classifier Combining Rules -- The “Test and Select” Approach to Ensemble Combination -- A Survey of Sequential Combination of Word Recognizers in Handwritten Phrase Recognition at CEDAR -- Multiple Classifier Combination Methodologies for Different Output Levels -- A Mathematically Rigorous Foundation for Supervised Learning -- Classifier Combinations: Implementations and Theoretical Issues -- Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification -- Complexity of Classification Problems and Comparative Advantages of Combined Classifiers -- Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems -- Combining Fisher Linear Discriminants for Dissimilarity Representations -- A Learning Method of Feature Selection for Rough Classification -- Analysis of a Fusion Method for Combining Marginal Classifiers -- A hybrid projection based and radial basis function architecture -- Combining Multiple Classifiers in Probabilistic Neural Networks -- Supervised Classifier Combination through Generalized Additive Multi-model -- Dynamic Classifier Selection -- Boosting in Linear Discriminant Analysis -- Different Ways of Weakening Decision Trees and Their Impact on Classification Accuracy of DT Combination -- Applying Boosting to Similarity Literals for Time Series Classification -- Boosting of Tree-Based Classifiers for Predictive Risk Modeling in GIS -- A New Evaluation Method for Expert Combination in Multi-expert System Designing -- Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems -- Self-Organizing Decomposition of Functions -- Classifier Instability and Partitioning -- A Hierarchical Multiclassifier System for Hyperspectral Data Analysis -- Consensus Based Classification of Multisource Remote Sensing Data -- Combining Parametric and Nonparametric Classifiers for an Unsupervised Updating of Land-Cover Maps -- A Multiple Self-Organizing Map Scheme for Remote Sensing Classification -- Use of Lexicon Density in Evaluating Word Recognizers -- A Multi-expert System for Dynamic Signature Verification -- A Cascaded Multiple Expert System for Verification -- Architecture for Classifier Combination Using Entropy Measures -- Combining Fingerprint Classifiers -- Statistical Sensor Calibration for Fusion of Different Classifiers in a Biometric Person Recognition Framework -- A Modular Neuro-Fuzzy Network for Musical Instruments Classification -- Classifier Combination for Grammar-Guided Sentence Recognition -- Shape Matching and Extraction by an Array of Figure-and-Ground Classifiers.

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