Ambroise C, Dang M, Govaert G. (1997). Clustering of spatial data by the EM algorithm Geostatistics For Environmental Applications. 3
Banerjee A, Mooney R, Basu S. (2002). Semi-supervised clustering by seeding Intl. Conf. on Machine Learning.
Bar-hillel A, Hertz T, Shental N, Weinshall D. (2004). Computing gaussian mixture models with EM using equivalence constraints Advances in neural information processing systems. 16
Basu S, Bilenko M, Mooney RJ. (2004). A probabilistic framework for semisupervised clustering Proc 10th ACM SIGKDD Intl Conf Knowledge Discovery and Data Mining.
Bouman CA, Shapiro M. (1994). A multiscale random field model for Bayesian image segmentation. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 3 [PubMed]
Buhmann J, Jain A, Lange T, Law M. (2005). Learning with constrained and unlabelled data Proc IEEE Computer Soc Conf Computer Vision and Pattern Recognition.
Dempster AP, Laird NM, Rubin DB. (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 39
Ghahramani Z, Zhu X, Lafferty J. (2003). Semi-supervised learning: From Gaussian field to gaussian processes Tech Rep CMU-CS-03-175, Computer Science Department CMU.
Hertz T, Shental N, Weinshall D, Bar-Hillel A. (2003). Computing gaussian mixture models with EM using side-information Tech Rep 2003-43, Leibniz Center for Research in Computer Science.
Jaakkola T. (2004). Tutorial on variational approximation methods Advanced mean field methods: Theory and practice.
Jaakkola T, Szummer M. (2001). Partially labeled classification with Markovrandom walks Advances in neural information processing systems. 14
Jain A, Law M, Topchy A. (2004). Clustering with soft and group constraints Joint IAPR International Workshop on Syntactical and Structural Pattern Recognition and Statistical Pattern Recognition.
Jain A, Law M, Topchy A. (2005). Model-based clustering with probabilistic constraints Proc SIAM Data Mining.
Jordan M, Russell S, Xing E, Ng A. (2003). Distance metric learning with application to clustering with side-information Advances in neural information processing systems. 15
Klein D, Kamvar S, Manning C. (2002). From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering Proc. of 19th International Conference on Machine Learning.
Lu Z, Leen T. (2005). Semi-supervised learning with penalized probabilistic clustering Advances in neural information processing systems. 17
Mccallum A, Cohn D, Caruana R. (2003). Semi-supervised clustering with user feedback Tech. Rep. TR2003-1892, Cornell University.
Mooney R, Basu S, Bilenko M. (2004). Integrating constraints and metric learning in semi-supervised clustering Proc 21st Intl Conf Mach Learn.
Neal RM. (1993). Probabilistic inference using Markov chain Monte Carlo methods Tech. Rep. No. CRG-TR-93-1.
Rogers S, Wagstaff K, Cardie C, Schroedl S. (2001). Constrained k-means clustering with background knowledge Proc. of 18th International Conference on Machine Learning .
Sill J, Abu-Mostafa Y. (1996). Monotonicity hints Advances in neural information processing systems. 8
Srivastava A, Oza NC, Stroeve J. (2005). Virtual sensors: Using data mining techniques to efficiently estimate remote sensing spectra IEEE Trans Geoscience And Remote Sensing. 43
Theiler J, Gisler G. (1997). A contiguity-enhanced K-means clustering algorithm for unsupervised multispectral image segementation Proc SPIE. 3159
Wagstaff K. (2002). Intelligent clustering with instance-level constraints Unbublished doctoral Dissertation.
Yarowsky D. (1995). Unsupervised word sense disambiguation rivaling supervised methods Proc 33rd Ann Mtg Assoc Computational Linguistics.
Zhou D, Scholkopf B. (2004). Learning from labeled and unlabeled data using random walks 26th Ann Mtg German Association for Pattern Recognition.