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Multiple Classifier Systems

Overview of attention for book
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 3: The “Test and Select” Approach to Ensemble Combination
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Chapter title
The “Test and Select” Approach to Ensemble Combination
Chapter number 3
Book title
Multiple Classifier Systems
Published in
Lecture notes in computer science, December 2000
DOI 10.1007/3-540-45014-9_3
Book ISBNs
978-3-54-067704-8, 978-3-54-045014-6
Authors

Sharkey, Amanda J. C., Sharkey, Noel E., Gerecke, Uwe, Chandroth, G. O., Amanda J. C. Sharkey, Noel E. Sharkey, Uwe Gerecke, G. O. Chandroth

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 5%
India 1 3%
Switzerland 1 3%
United Kingdom 1 3%
United States 1 3%
Unknown 31 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 24%
Student > Ph. D. Student 7 19%
Professor > Associate Professor 5 14%
Student > Master 4 11%
Student > Doctoral Student 3 8%
Other 5 14%
Unknown 4 11%
Readers by discipline Count As %
Computer Science 16 43%
Engineering 11 30%
Biochemistry, Genetics and Molecular Biology 1 3%
Business, Management and Accounting 1 3%
Mathematics 1 3%
Other 2 5%
Unknown 5 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 September 2016.
All research outputs
#7,523,962
of 22,961,203 outputs
Outputs from Lecture notes in computer science
#2,489
of 8,137 outputs
Outputs of similar age
#26,465
of 114,481 outputs
Outputs of similar age from Lecture notes in computer science
#8
of 24 outputs
Altmetric has tracked 22,961,203 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,137 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 54% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 114,481 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.