Multiple Classifier Systems Second International Workshop, MCS 2001 Cambridge, UK, July 2-4, 2001 Proceedings /
Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the deve...
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2001.
|
| Έκδοση: | 1st ed. 2001. |
| Σειρά: | Lecture Notes in Computer Science,
2096 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- Bagging and Boosting
- Bagging and the Random Subspace Method for Redundant Feature Spaces
- Performance Degradation in Boosting
- A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models
- Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis
- Learning Classification RBF Networks by Boosting
- MCS Design Methodology
- Data Complexity Analysis for Classifier Combination
- Genetic Programming for Improved Receiver Operating Characteristics
- Methods for Designing Multiple Classifier Systems
- Decision-Level Fusion in Fingerprint Verification
- Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition
- Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'
- Averaging Weak Classifiers
- Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds
- Ensemble Classifiers
- Multiple Classifier Systems Based on Interpretable Linear Classifiers
- Least Squares and Estimation Measures via Error Correcting Output Code
- Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis
- Information Analysis of Multiple Classifier Fusion?
- Limiting the Number of Trees in Random Forests
- Learning-Data Selection Mechanism through Neural Networks Ensemble
- A Multi-SVM Classification System
- Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System
- Feature Spaces for MCS
- Feature Weighted Ensemble Classifiers - A Modified Decision Scheme
- Feature Subsets for Classifier Combination: An Enumerative Experiment
- Input Decimation Ensembles: Decorrelation through Dimensionality Reduction
- Classifier Combination as a Tomographic Process
- MCS in Remote Sensing
- A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps
- Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances
- Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data
- Solar Wind Data Analysis Using Self-Organizing Hierarchical Neural Network Classifiers
- One Class MCS and Clustering
- Combining One-Class Classifiers
- Finding Consistent Clusters in Data Partitions
- A Self-Organising Approach to Multiple Classifier Fusion
- Combination Strategies
- Error Rejection in Linearly Combined Multiple Classifiers
- Relationship of Sum and Vote Fusion Strategies
- Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation
- On Combining Dissimilarity Representations
- Application of Multiple Classifier Techniques to Subband Speaker Identification with an HMM/ANN System
- Classification of Time Series Utilizing Temporal and Decision Fusion
- Use of Positional Information in Sequence Alignment for Multiple Classifier Combination
- Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting
- Tree-Structured Support Vector Machines for Multi-class Pattern Recognition
- On the Combination of Different Template Matching Strategies for Fast Face Detection
- Improving Product by Moderating k-NN Classifiers
- Automatic Model Selection in a Hybrid Perceptron/Radial Network.
