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. |
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
accuracy algorithm approach approximation assigned assume average bagging banana bias calculated called class label classifier clustering combination competence consider containing correct corresponding data set decision Denote depend derived distance distribution diversity ensemble error estimate example experiment feature Figure function given gives improvement independent individual input layer learning Machine majority vote matrix mean measure method multiple needed neighbor Neural Networks node normal Note objects obtained optimal outputs pairs parameters partitions pattern recognition percent plot points possible posterior probabilities probability problem procedure produces proposed prototypes pruning random regions respective rule sample selection shown shows similarity simple single space split statistic step subsets suggest Systems Table testing training set tree true variable variance vector weights