Generative embeddings based on Rician mixtures for kernel-based classification of magnetic resonance images
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Mário A. T. Figueiredo | Manuele Bicego | Vittorio Murino | Anna C. Carli | Vittorio Murino | M. Bicego | A. C. Carli
[1] Kiyoshi Asai,et al. Marginalized kernels for biological sequences , 2002, ISMB.
[2] S. Rice. Mathematical analysis of random noise , 1944 .
[3] Paul D. McNicholas,et al. Model-Based Clustering , 2016, Journal of Classification.
[4] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[5] Jean-Philippe Vert,et al. Semigroup Kernels on Finite Sets , 2004, NIPS.
[6] Eric P. Xing,et al. Nonextensive Information Theoretic Kernels on Measures , 2009, J. Mach. Learn. Res..
[7] C. Watkins. Dynamic Alignment Kernels , 1999 .
[8] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[9] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[10] U. Castellani,et al. Schizophrenia classification using regions of interest in brain MRI , 2009, IDA 2009.
[11] Kenji Fukumizu,et al. Semigroup Kernels on Measures , 2005, J. Mach. Learn. Res..
[12] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[13] David Haussler,et al. Convolution kernels on discrete structures , 1999 .
[14] Snehashis Roy,et al. A Rician mixture model classification algorithm for magnetic resonance images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[15] Volodymyr Melnykov,et al. Finite mixture models and model-based clustering , 2010 .
[16] Michael I. Jordan,et al. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.
[17] Thorsten Gerber,et al. Handbook Of Mathematical Functions , 2016 .
[18] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[19] Alexander J. Smola,et al. Learning with kernels , 1998 .
[20] Mário A. T. Figueiredo,et al. Generative Embeddings based on Rician Mixtures - Application to Kernel-based Discriminative Classification of Magnetic Resonance Images , 2012, ICPRAM.
[21] C. R. Rao,et al. On the convexity of some divergence measures based on entropy functions , 1982, IEEE Trans. Inf. Theory.
[22] Ranjan Maitra,et al. On the Expectation-Maximization algorithm for Rice-Rayleigh mixtures with application to noise parameter estimation in magnitude MR datasets , 2013, Sankhya B.
[23] P. Deb. Finite Mixture Models , 2008 .
[24] Trevor J. Hastie,et al. Discriminative vs Informative Learning , 1997, KDD.
[25] Hiroki Suyari. Generalization of Shannon-Khinchin axioms to nonextensive systems and the uniqueness theorem for the nonextensive entropy , 2004, IEEE Transactions on Information Theory.
[26] B. P. Lathi,et al. Modern Digital and Analog Communication Systems , 1983 .
[27] Tom Minka,et al. Principled Hybrids of Generative and Discriminative Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[28] Tony Jebara,et al. A Kernel Between Sets of Vectors , 2003, ICML.
[29] Adrian E. Raftery,et al. Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .
[30] Nuno Vasconcelos,et al. A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications , 2003, NIPS.
[31] Tony Jebara,et al. Probability Product Kernels , 2004, J. Mach. Learn. Res..
[32] Ranjan Maitra,et al. Synthetic Magnetic Resonance Imaging Revisited , 2010, IEEE Transactions on Medical Imaging.
[33] Ranjan Maitra,et al. Noise Estimation in Magnitude MR Datasets , 2009, IEEE Transactions on Medical Imaging.
[34] Milton Abramowitz,et al. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .
[35] Matthias Hein,et al. Hilbertian Metrics and Positive Definite Kernels on Probability Measures , 2005, AISTATS.
[36] J. Alison Noble,et al. Statistical 3D Vessel Segmentation Using a Rician Distribution , 1999, MICCAI.
[37] R. Henkelman. Measurement of signal intensities in the presence of noise in MR images. , 1985, Medical physics.
[38] Manuele Bicego,et al. A Hybrid Generative/Discriminative Method for Classification of Regions of Interest in Schizophrenia Brain MRI , 2009 .
[39] Anil K. Jain,et al. Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[40] G. McLachlan,et al. The EM algorithm and extensions , 1996 .
[41] Bernhard Schölkopf,et al. Dynamic Alignment Kernels , 2000 .
[42] Robert P. W. Duin,et al. Dissimilarity-Based Detection of Schizophrenia , 2010, 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging.
[43] David Haussler,et al. Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.
[44] H. Gudbjartsson,et al. The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.
[45] C. Tsallis. Possible generalization of Boltzmann-Gibbs statistics , 1988 .