Multiple classifier systems : First International Workshop, MCS 2000 Cagliari, Italy, June 21 23, 2000 : proceedings
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| Format: | Livre numérique |
| Język: | Anglais |
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Springer
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Cham : Springer Nature |
| Seria: | Lecture notes in computer science
1857 |
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| Edition sous un autre format: | • Multiple classifier systems, First International Workshop, MCS 2000, Cagliari, Italy, June 2000, Proceedings, Josef Kittler, Fabio Roli (eds.), Berlin, Springer, 2000, 1 vol. (XII-404 p.), Lecture notes in computer science, 3-540-67704-6 • Multiple Classifier Systems, Texte imprimé, 9783662168264 |
Spis treści:
- 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.