References and models cited by this paper
References and models that cite this paper
Akaike H (1973) Information theory and an extension of the maximum likelihood principle 2nd Intl Symp in Information Theory, Petrov BN:Csaki F, ed. pp.267
Bar-hillel A, Hertz T, Shental N, Weinshall D (2003) Learning distance functions using equivalence relations Proc. of 20th International Conference on Machine Learning
Bar-Hillel A, Hertz T, Shental N, Weinshall D (2003) Learning via equivalence constraints, with applications to the enhancement of image and video retrieval Proc. of IEEE Conference on Computer Vision and Pattern Recognition
Basu S, Banerjee A, Mooney R (2002) Semi-supervised clustering by seeding Intl. Conf. on Machine Learning :19-26
Basu S, Bilenko M, Mooney R (2003) Comparing and unifying search-based and similarity-based approaches to semi-supervised clustering Proc. of ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining Systems :42-49
Besag J (1986) On the Statistical Analysis of Dirty Pictures J Roy Stat Soc B 48:259-302
Blum A, Mitchell T (1998) Combined labeled and unlabeled data with co-training Proceedings of Computational Learning Theory (COLT 98)
Demiriz A, Bennett K (2001) Optimization approaches to semi-supervised learning Complementarity: Applications, algorithms and extensions, Fernis M, ed.
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39:1-38
Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning
Hofmann T, Buhmann JM (1997) Pairwise data clustering by deterministic annealing IEEE Transactions On Pattern Analysis And Machine Intelligen 19:1-25
Jaakkola T, Meila M, Jebara T (1999) Maximum entropy discrimination Advances in neural information processing systems, Solla SA:Leen TK:Muller KR, ed.
Joachims T (1999) Transductive inference for text classification using support vector machines Proc. of the Fourteenth Conference on Uncertainty in AI :200-209
Kindermann R, Snell J (1980) Markov random fields and their applications
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
Law M, Topchy A, Jain A (2004) Clustering with soft and group constraints Joint IAPR International Workshop on Syntactical and Structural Pattern Recognition and Statistical Pattern Recognition, Fred A:Caelli T:Duin RPW, ed.
Mclachlan GJ, Peel D (2000) Finite mixture models
Miller D, Browning J (2003) A mixture model and EM-based algorithm for class discovery, robust classification, and outlier rejection in mixed labeled-unlabeled data sets IEEE Trans Pattern Anal Mach Intell 25:1468-1483
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, Mozer M:Jordan J:Petsche T, ed. pp.571
Neal RM, Hinton GE (1998) A new view of the EM algorithm that justifies incremental, sparse and other variants Learning in graphical models, Jordan MI, ed. pp.355
Nigam K, Mccallum A, Thrun S, Mitchell T (2000) Text classification from labeled and unlabeled documents using EM Mach Learn 39:1-34
Rissanen J (1978) Modeling by shortest data description Automatica 14:465-471
Rose K (1998) Deterministic annealing for clustering, compression, classification, regression, and related optimization problems Proceedings Of The IEEE 86:2210-2239
Schwarz G (1978) Estimating the dimension of a model Ann Stat 6:461-464
Seeger M (2000) Learning with labeled and unlabeled data Tech Rep
Shashahani B, Landgrebe D (1994) The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon IEEE Trans Geoscience And Remote Sensing 32:1087-1095
Shental N, Bar-Hillel A, Hertz T, Weinshall D (2003) Computing gaussian mixture models with EM using equivalence constraints Advances in neural information processing systems, Becker S:Thrun S:Obermayer K, ed.
Stork DG (2001) Toward a computational theory of data acquisition and truthing Proceedings of Computational Learning Theory (COLT 01)
Wagstaff K (2002) Intelligent clustering with instance-level constraints Unbublished doctoral Dissertation
Wagstaff K, Cardie C, Rogers S, Schroedl S (2001) Constrained k-means clustering with background knowledge Proc. of 18th International Conference on Machine Learning :577-584
Xing E, Ng A, Jordan M, Russell S (2003) Distance metric learning with application to clustering with side-information Advances in neural information processing systems, Becker S:Thrun S:Obermayer K, ed.
Yuille A, Stolorz P, Utans J (1994) Statistical physics, mixtures of distributions, and the EM algorithm Neural Comput 6:334-340