Combining Pattern Classifiers: Methods and AlgorithmsCovering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading. |
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Results 1-5 of 28
Page ix
... Pairwise Classification / 249 8.2.5 Comments and Further Directions / 250 8.3 Combining Clustering Results / 251 8.3.1 Measuring Similarity Between Partitions / 251 8.3.2 Evaluating Clustering Algorithms / 254 Cluster Ensembles / 257 ...
... Pairwise Classification / 249 8.2.5 Comments and Further Directions / 250 8.3 Combining Clustering Results / 251 8.3.1 Measuring Similarity Between Partitions / 251 8.3.2 Evaluating Clustering Algorithms / 254 Cluster Ensembles / 257 ...
Page x
... Pairwise Measures / 300 10.2.2 Nonpairwise Measures / 301 10.3 Relationship Between Diversity and Accuracy / 306 10.3.1 Example / 306 10.3.2 Relationship Patterns / 309 10.4 Using Diversity / 314 10.4.1 Diversity for Finding Bounds and ...
... Pairwise Measures / 300 10.2.2 Nonpairwise Measures / 301 10.3 Relationship Between Diversity and Accuracy / 306 10.3.1 Example / 306 10.3.2 Relationship Patterns / 309 10.4 Using Diversity / 314 10.4.1 Diversity for Finding Bounds and ...
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Contents
1 Fundamentals of Pattern Recognition | 1 |
2 Base Classifiers | 45 |
3 Multiple Classifier Systems | 101 |
4 Fusion of Label Outputs | 111 |
5 Fusion of ContinuousValued Outputs | 151 |
6 Classifier Selection | 189 |
7 Bagging and Boosting | 203 |
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Common terms and phrases
AdaBoost added error approximation bagging banana data base classifiers Bayes bias boosting calculated clas class label classification regions classifier combination classifier ensembles classifier models classifier outputs combining classifiers confusion matrix convex hull correct data points data set decision templates decision trees Denote discriminant functions DP(x ECOC error rate estimate example feature space feature subsets fuzzy Hamming distance IEEE Transactions individual accuracy input Jaccard index jxà k-nn Kittler Kuncheva linear classifier Machine Learning method Multiple Classifier Systems nearest neighbor Neural Networks node normal distribution number of classifiers number of clusters objects optimal pairs pairwise parameters partitions Parzen Pattern Recognition percent perceptron Pmaj posterior probabilities prior probabilities problem Proc prototypes pruning Rand index random random forest randomly Roli sample sifier split Table testing error training error training set tree classifier variable variance vector w₁ weighted average Workshop on Multiple