Combining pattern classifiers : methods and algorithms /
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| Author / Creator: | Kuncheva, Ludmila I. (Ludmila Ilieva), 1959- |
|---|---|
| Imprint: | Hoboken, NJ : J. Wiley, 2004. |
| Description: | xx, 350 p. : ill. ; 24 cm. |
| Language: | English |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/5541514 |
Table of Contents:
- Preface
- Acknowledgments
- Notations and Acronyms
- 1. Fundamentals of Pattern Recognition
- 1.1. Basic Concepts: Class, Feature, Data Set
- 1.2. Classifier, Discriminant Functions, Classification Regions
- 1.3. Classification Error and Classification Accuracy
- 1.4. Experimental Comparison of Classifiers
- 1.5. Bayes Decision Theory
- 1.6. A Taxonomy of Classifier Design Methods
- 1.7. Clustering
- Appendix
- 2. Base Classifiers
- 2.1. Linear and Quadratic Classifiers
- 2.2. Nonparametric Classifiers
- 2.3. The k-nearest Neighbor Rule
- 2.4. Tree Classifiers
- 2.5. Neural Networks
- Appendix
- 3. Multiple Classifier Systems
- 3.1. Philosophy
- 3.2. Terminologies and Taxonomies
- 3.3. To Train or Not to Train?
- 3.4. Remarks
- 4. Fusion of Label Outputs
- 4.1. Types of Classifier Outputs
- 4.2. Majority Vote
- 4.3. Weighted Majority Vote
- 4.4. "Naïve"-Bayes Combination
- 4.5. Multinomial Methods
- 4.6. Probabilistic Approximation
- 4.7. SVD Combination
- 4.8. Conclusions
- Appendix
- 5. Fusion of Continuous-Valued Outputs
- 5.1. How Do We Get Probability Outputs?
- 5.2. Class-Conscious Combiners
- 5.3. Class-Indifferent Combiners
- 5.4. Where Do the Simple Combiners Come From?
- 5.5. Appendix
- 6. Classifier Selection
- 6.1. Preliminaries
- 6.2. Why Classifier Selection Works
- 6.3. Estimating Local Competence Dynamically
- 6.4. Pre-estimation of the Competence Regions
- 6.5. Selection or Fusion?
- 6.6. Base Classifiers and Mixture of Experts
- 7. Bagging and Boosting
- 7.1. Bagging
- 7.2. Boosting
- 7.3. Bias-Variance Decomposition
- 7.1. Which is Better: Bagging or Boosting?
- Appendix
- 8. Miscellanea
- 8.1. Feature Selection
- 8.2. Error Correcting Output Codes (ECOC
- 8.3. Combining Clustering Results
- Appendix
- 9. Theoretical Views and Results
- 9.1. Equivalence of Simple Combination Rules
- 9.2. Added Error for the Mean Combination Rule
- 9.3. Added Error for the Weighted Mean Combination
- 9.4. Ensemble Error for Normal and Uniform Distributions
- 10. Diversity in Classifier Ensembles
- 10.1. What is Diversity?
- 10.2. Measuring Diversity in Classifier Ensembles
- 10.3. Relationship Between Diversity and Accuracy
- 10.4. Using Diversity
- 10.5. Conclusions: Diversity of Diversity
- Appendix A. Equivalence Between the Averaged Disagreement Measure D av and Kohavi-Wolpert KW
- Appendix B. Matlab Code for Some Overproduce and Select Algorithms
- References
- Index
