Multiple Classifier Systems First International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Kittler, Josef (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Roli, Fabio (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2000.
• Έκδοση: 1st ed. 2000.
• Σειρά: Lecture Notes in Computer Science, 1857
• Θέματα: Pattern recognition.; Artificial intelligence.; Optical data processing.; Algorithms.; Computers.; Pattern Recognition.; Artificial Intelligence.; Image Processing and Computer Vision.; Algorithm Analysis and Problem Complexity.; Computation by Abstract Devices.

Πίνακας περιεχομένων:

• 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.