|
|
|
Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings
|
(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!
| Inhalt: |
| 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. |
|
i.d.R. innert 5-10 Tagen versandfertig
Newsfeed | 
Facebook | 
Twitter
