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Multiple Classifier Systems

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Cover of 'Multiple Classifier Systems'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Ensemble Methods in Machine Learning
  3. Altmetric Badge
    Chapter 2 Experiments with Classifier Combining Rules
  4. Altmetric Badge
    Chapter 3 The “Test and Select” Approach to Ensemble Combination
  5. Altmetric Badge
    Chapter 4 A Survey of Sequential Combination of Word Recognizers in Handwritten Phrase Recognition at CEDAR
  6. Altmetric Badge
    Chapter 5 Multiple Classifier Combination Methodologies for Different Output Levels
  7. Altmetric Badge
    Chapter 6 A Mathematically Rigorous Foundation for Supervised Learning
  8. Altmetric Badge
    Chapter 7 Classifier Combinations: Implementations and Theoretical Issues
  9. Altmetric Badge
    Chapter 8 Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification
  10. Altmetric Badge
    Chapter 9 Complexity of Classification Problems and Comparative Advantages of Combined Classifiers
  11. Altmetric Badge
    Chapter 10 Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems
  12. Altmetric Badge
    Chapter 11 Combining Fisher Linear Discriminants for Dissimilarity Representations
  13. Altmetric Badge
    Chapter 12 A Learning Method of Feature Selection for Rough Classification
  14. Altmetric Badge
    Chapter 13 Analysis of a Fusion Method for Combining Marginal Classifiers
  15. Altmetric Badge
    Chapter 14 A hybrid projection based and radial basis function architecture
  16. Altmetric Badge
    Chapter 15 Combining Multiple Classifiers in Probabilistic Neural Networks
  17. Altmetric Badge
    Chapter 16 Supervised Classifier Combination through Generalized Additive Multi-model
  18. Altmetric Badge
    Chapter 17 Dynamic Classifier Selection
  19. Altmetric Badge
    Chapter 18 Boosting in Linear Discriminant Analysis
  20. Altmetric Badge
    Chapter 19 Different Ways of Weakening Decision Trees and Their Impact on Classification Accuracy of DT Combination
  21. Altmetric Badge
    Chapter 20 Applying Boosting to Similarity Literals for Time Series Classification
  22. Altmetric Badge
    Chapter 21 Boosting of Tree-Based Classifiers for Predictive Risk Modeling in GIS
  23. Altmetric Badge
    Chapter 22 A New Evaluation Method for Expert Combination in Multi-expert System Designing
  24. Altmetric Badge
    Chapter 23 Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems
  25. Altmetric Badge
    Chapter 24 Self-Organizing Decomposition of Functions
  26. Altmetric Badge
    Chapter 25 Classifier Instability and Partitioning
  27. Altmetric Badge
    Chapter 26 A Hierarchical Multiclassifier System for Hyperspectral Data Analysis
  28. Altmetric Badge
    Chapter 27 Consensus Based Classification of Multisource Remote Sensing Data
  29. Altmetric Badge
    Chapter 28 Combining Parametric and Nonparametric Classifiers for an Unsupervised Updating of Land-Cover Maps
  30. Altmetric Badge
    Chapter 29 A Multiple Self-Organizing Map Scheme for Remote Sensing Classification
  31. Altmetric Badge
    Chapter 30 Use of Lexicon Density in Evaluating Word Recognizers
  32. Altmetric Badge
    Chapter 31 A Multi-expert System for Dynamic Signature Verification
  33. Altmetric Badge
    Chapter 32 A Cascaded Multiple Expert System for Verification
  34. Altmetric Badge
    Chapter 33 Architecture for Classifier Combination Using Entropy Measures
  35. Altmetric Badge
    Chapter 34 Combining Fingerprint Classifiers
  36. Altmetric Badge
    Chapter 35 Statistical Sensor Calibration for Fusion of Different Classifiers in a Biometric Person Recognition Framework
  37. Altmetric Badge
    Chapter 36 A Modular Neuro-Fuzzy Network for Musical Instruments Classification
  38. Altmetric Badge
    Chapter 37 Classifier Combination for Grammar-Guided Sentence Recognition
  39. Altmetric Badge
    Chapter 38 Shape Matching and Extraction by an Array of Figure-and-Ground Classifiers
Attention for Chapter 1: Ensemble Methods in Machine Learning
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Chapter title
Ensemble Methods in Machine Learning
Chapter number 1
Book title
Multiple Classifier Systems
Published by
Springer, Berlin, Heidelberg, June 2000
DOI 10.1007/3-540-45014-9_1
Book ISBNs
978-3-54-067704-8, 978-3-54-045014-6
Authors

Thomas G. Dietterich, Dietterich, Thomas G.

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X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3,660 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 34 <1%
United Kingdom 16 <1%
India 9 <1%
Germany 8 <1%
Spain 8 <1%
China 8 <1%
France 7 <1%
Malaysia 6 <1%
Ireland 6 <1%
Other 76 2%
Unknown 3482 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 804 22%
Student > Master 663 18%
Researcher 347 9%
Student > Bachelor 290 8%
Student > Doctoral Student 154 4%
Other 589 16%
Unknown 813 22%
Readers by discipline Count As %
Computer Science 1321 36%
Engineering 541 15%
Agricultural and Biological Sciences 102 3%
Mathematics 92 3%
Business, Management and Accounting 73 2%
Other 583 16%
Unknown 948 26%