Alpaydin E. (1999). Combined 5 x 2 cv F test for comparing supervised classification learning algorithms. Neural computation. 11 [PubMed]
Basak J, Kothari R. (2004). A classification paradigm for distributed vertically partitioned data. Neural computation. 16 [PubMed]
Breiman L. (1996). Bagging predictors Mach Learn. 24
Chen K, Wang L, Chi H. (1997). Methods of combining multiple classifiers with different features and their applications to text independent speaker identification Intl J Of Patt Rec And Art Intell. 11
Cover TM, Thomas JA. (1991). Elements of Information Theory.
Dcosta A, Ramachandran V, Sayeed A. (2004). Distributed classification of gaussian space-time sources in wireless sensor networks IEEE J Sel Areas Comm. 22
Della_Pietra VJ, Della_Pietra S, Lafferty JD. (1997). Inducing features of random fields IEEE Trans Patt Anal Mach Intell. 19
Dempster AP, Laird NM, Rubin DB. (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 39
Dietterich TG, Kong EB. (1995). Error-correcting output coding corrects bias and variance Proc 12th Intl Conf Mach Learn.
Freund Y, Schapire R. (1996). Experiment with a new boosting algorithm Proc. of the 13th International Conference on Machine Learning.
Ghosh J, Tumer K. (1996). Error correlation and error reduction in ensemble classifiers Connection Science. 8
Ghosh J, Tumer K. (2002). Robust combining of disparate classifiers through order statistics Patt Anal Apps. 5
Hansen LK, Salamon P. (1990). Neural network ensembles IEEE Trans Pattern Anal Machine Intell. 12
Jacobs RA, Hinton GE, Jordan MI, Nowlan SJ. (1991). Adaptive mixtures of local experts Neural Comput. 3
Jaynes ET. (1989). Papers on probability, statistics, and statistical physics (2nd ed).
Joachims T. (1999). Transductive inference for text classification using support vector machines Proc. of the Fourteenth Conference on Uncertainty in AI.
Kang HJ, Kimk KIM. (1997). Optimal approximation of discrete probability distribution with kth-order dependency and its application to combining multiple classifiers Patt Rec Lett. 18
Landgrebe DA. (2005). Multispectral land sensing: Where from, where to? IEEE Trans Geo Rem Sens. 43
Mclachlan GJ, Peel D. (2000). Finite mixture models.
Miller D, Uyar H. (1997). A mixture of experts classifier with learning based on both labelled and unlabelled data Advances in neural information processing systems. 9
Miller DJ, Pal S. (2005). An extension of iterative scaling for joint decision-leveland feature-level fusion in ensemble classification Intl Workshop Mach Learn Signal Process.
Miller DJ, Yan L. (1999). Critic-driven ensemble classification IEEE Trans Sig Proc. 47
Miller DJ, Yan L. (2000). Approximate maximum entropy joint feature inference consistent with arbitrary lower-order probability constraints: application to statistical classification Neural computation. 12 [PubMed]
Redner RA, Walker HF. (1984). Mixture densities, maximum likelihood and the EM algorithm SIAM Rev. 26
Saerens M, Latinne P, Decaestecker C. (2002). Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure. Neural computation. 14 [PubMed]
Varshney P. (1997). Distributed detection and data fusion.
Xu L, Krzyzak A, Suen CY. (1992). Methods of combining multiple classifiers and their applications to handwriting recognition IEEE Trans Syst Man Cybern. 22