Combining Pattern Classifiers: Methods and Algorithms
A unified, coherent, and expansive treatment of current classifier ensemble methods Mail sorting, medical test reading, military target recognition, signature verification, meteorological forecast, DNA matching, fingerprint recognition. These are some of the areas requiring reliable, precise pattern recognition. 'Combining pattern classifiers - methods and algorithms' represents the first attempt to provide a comprehensive survey of this fast-growing field. In a clear and straightforward manner, the author provides a much-needed road map through a multifaceted and often controversial subject while effectively organizing and systematizing the current state of the art. Covering a broad range of methodologies, algorithms, and theories, the text is replete with case studies and real-world applications, and will be of interest to academics and researchers in the field seeking both new classification tools and new uses for the old ones.
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Multiple Classifier Systems
Fusion of Label Outputs
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AdaBoost added error approximation assigned assume bagging banana data base classifiers Bayes bias calculated clas class label classification regions classifier ensembles classifier models classifier outputs classifier selection clustering algorithm coefficients confusion matrix convex hull correct cross-validation data points data set decision templates decision tree Denote discriminant functions DP(x ECOC error rate estimate example feature space feature subsets Hamming distance hidden layer individual accuracy individual classifiers input Jaccard index k-nn linear classifier Matlab method minimum misclassified nearest neighbor neural networks node noise normal distribution number of classifiers number of clusters objects obtained optimal pairs pairwise parameters partitions pattern recognition percent plot Pmaj posterior probabilities prior probabilities problem prototypes pruning Rand index random random forest randomly sample shown in Figure sifier split Table testing error training and testing training data training error training set tree classifier variable variance vector weighted average