Multiple Classifier Systems Book

	Classifier ensembles for changing environments	1
	A generic sensor fusion problem : classification and function estimation	16
	AveBoost2 : boosting for noisy data	31
	Bagging decision multi-trees	41
	Learn++.MT : a new approach to incremental learning	52
	Beyond boosting : recursive ECOC learning machines	62
	Exact bagging with k-nearest neighbour classifiers	72
	Yet another method for combining classifiers outputs : a maximum entropy approach	82
	Combining one-class classifiers to classify missing data	92
	Combining kernel information for support vector classification	102
	Combining classifiers using dependency-based product approximation with Bayes error rate	112
	Combining dissimilarity-based one-class classifiers	122
	A modular system for the classification of time series data	134
	A probabilistic model using information theoretic measures for cluster ensembles	144
	Classifier fusion using triangular norms	154
	Dynamic integration of regression models	164
	Dynamic classifier selection by adaptive k-nearest-neighbourhood rule	174
	Spectral measure for multi-class problems	184
	The relationship between classifier factorisation and performance in stochastic vector quantisation	194
	A method for designing cost-sensitive ECOC	204
	Building graph-based classifier ensembles for random node selection	214
	A comparison of ensemble creation techniques	223
	Multiple classifiers system for reducing influences of atypical observations	233
	Sharing training patterns among multiple classifiers	243
	First experiments on ensembles of radial basis functions	253
	Random aggregated and bagged ensembles of SVMs : an empirical bias-variance analysis	263
	Building diverse classifier outputs to evaluate the behavior of combination methods : the case of two classifiers	273
	An empirical comparison of hierarchical vs. two-level approaches to multiclass problems	283
	Experiments on ensembles with missing and noisy data	293
	Induced decision fusion in automated sign language interpretation : using ICA to isolate the underlying components of sign	303
	Ensembles of classifiers derived from multiple prototypes and their application to handwriting recognition	314
	Network intrusion detection by a multi-stage classification system	324
	Application of Breiman's random forest to modeling structure-activity relationships of pharmaceutical molecules	334
	Experimental study on multiple LDA classifier combination for high dimensional data classification	344
	Physics-based decorrelation of image data for decision level fusion in face verification	354
	High security fingerprint verification by perceptron-based fusion of multiple matchers	364
	Second guessing a commercial 'black box' classifier by an 'in house' classifier : serial classifier combination in a speech recognition application	374