Bayesian network modeling of the consensus between experts: An application to neuron classification
暂无分享,去创建一个
Concha Bielza | Pedro Larrañaga | Javier DeFelipe | Pedro L. López-Cruz | C. Bielza | P. Larrañaga | J. DeFelipe
[1] Concha Bielza,et al. New insights into the classification and nomenclature of cortical GABAergic interneurons , 2013, Nature Reviews Neuroscience.
[2] Kevin B. Korb,et al. Incorporating expert knowledge when learning Bayesian network structure: A medical case study , 2011, Artif. Intell. Medicine.
[3] Qiang Shen,et al. Learning Bayesian networks: approaches and issues , 2011, The Knowledge Engineering Review.
[4] José M. Peña,et al. Finding Consensus Bayesian Network Structures , 2011, J. Artif. Intell. Res..
[5] T. Pham-Gia,et al. Clustering probability distributions , 2010 .
[6] Joe Michael Kniss,et al. Representing Diversity in Communities of Bayesian Decision-makers , 2010, 2010 IEEE Second International Conference on Social Computing.
[7] Xuelong Li,et al. A survey of graph edit distance , 2010, Pattern Analysis and Applications.
[8] Duc Truong Pham,et al. Unsupervised training of Bayesian networks for data clustering , 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[9] Joe Michael Kniss,et al. Satisficing the Masses: Applying Game Theory to Large-Scale, Democratic Decision Problems , 2009, 2009 International Conference on Computational Science and Engineering.
[10] Marco Scutari,et al. Learning Bayesian Networks with the bnlearn R Package , 2009, 0908.3817.
[11] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[12] René Vidal,et al. Unsupervised Riemannian Clustering of Probability Density Functions , 2008, ECML/PKDD.
[13] Xiaotong Shen,et al. Variable Selection in Penalized Model‐Based Clustering Via Regularization on Grouped Parameters , 2008, Biometrics.
[14] E. P. Gardner,et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex , 2008, Nature Reviews Neuroscience.
[15] Larry W. Swanson,et al. The neuron classification problem , 2007, Brain Research Reviews.
[16] Nizar Bouguila,et al. High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] G. Ascoli,et al. NeuroMorpho.Org: A Central Resource for Neuronal Morphologies , 2007, The Journal of Neuroscience.
[18] Bo Chen,et al. A Clustering Based Bayesian Network Classifier , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).
[19] Giorgio A. Ascoli,et al. Successes and Rewards in Sharing Digital Reconstructions of Neuronal Morphology , 2007, Neuroinformatics.
[20] Doheon Lee,et al. Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality , 2007, IEICE Trans. Inf. Syst..
[21] Wei Pan,et al. Penalized Model-Based Clustering with Application to Variable Selection , 2007, J. Mach. Learn. Res..
[22] M. Chacron,et al. Neural Variability, Detection Thresholds, and Information Transmission in the Vestibular System , 2007, The Journal of Neuroscience.
[23] A. O'Hagan,et al. Statistical Methods for Eliciting Probability Distributions , 2005 .
[24] Kelvin E. Jones,et al. Neuronal variability: noise or part of the signal? , 2005, Nature Reviews Neuroscience.
[25] A. Robles-Kelly,et al. Graph Edit Distance from Spectral Seriation , 2005 .
[26] Gregory F. Cooper,et al. Model Averaging for Prediction with Discrete Bayesian Networks , 2004, J. Mach. Learn. Res..
[27] Nizar Bouguila,et al. Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application , 2004, IEEE Transactions on Image Processing.
[28] Eugene Santos,et al. Exploring Case-Based Bayesian Networks and Bayesian Multi-nets for Classification , 2004, Canadian Conference on AI.
[29] K. Sivakumar,et al. Collective Mining of Bayesian Networks from Distributed Heterogeneous Data , 2004 .
[30] Serafín Moral,et al. Qualitative combination of Bayesian networks , 2003, Int. J. Intell. Syst..
[31] Michael I. Jordan,et al. Learning with Mixtures of Trees , 2001, J. Mach. Learn. Res..
[32] Pedrito Maynard-Reid,et al. Aggregating Learned Probabilistic Beliefs , 2001, UAI.
[33] Russell Greiner,et al. Learning Bayesian Belief Network Classifiers: Algorithms and System , 2001, Canadian Conference on AI.
[34] Pedro Larrañaga,et al. Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[35] Michael P. Wellman,et al. Graphical Representations of Consensus Belief , 1999, UAI.
[36] David Heckerman,et al. A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.
[37] R. C Cannon,et al. An on-line archive of reconstructed hippocampal neurons , 1998, Journal of Neuroscience Methods.
[38] Bo Thiesson,et al. Learning Mixtures of DAG Models , 1998, UAI.
[39] Tom Heskes,et al. Selecting Weighting Factors in Logarithmic Opinion Pools , 1997, NIPS.
[40] Huaiyu Zhu. On Information and Sufficiency , 1997 .
[41] Pedro Larrañaga,et al. Learning Bayesian network structures by searching for the best ordering with genetic algorithms , 1996, IEEE Trans. Syst. Man Cybern. Part A.
[42] David Heckerman,et al. Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets , 1996, Artif. Intell..
[43] Wray L. Buntine. A Guide to the Literature on Learning Probabilistic Networks from Data , 1996, IEEE Trans. Knowl. Data Eng..
[44] Bruce Abramson,et al. Deriving A Minimal itI-map of a Belief Network Relative to a Target Ordering of its Nodes , 1993, UAI.
[45] Tao Yang,et al. A Comparison of Clustering Heuristics for Scheduling Directed Acycle Graphs on Multiprocessors , 1992, J. Parallel Distributed Comput..
[46] Gregory F. Cooper,et al. A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.
[47] Bruce Abramson,et al. The Topological Fusion of Bayes Nets , 1992, UAI.
[48] David J. Spiegelhalter,et al. Local computations with probabilities on graphical structures and their application to expert systems , 1990 .
[49] Christian Genest,et al. Combining Probability Distributions: A Critique and an Annotated Bibliography , 1986 .
[50] R. Bordley. A Multiplicative Formula for Aggregating Probability Assessments , 1982 .
[51] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[52] G. Yule. NOTES ON THE THEORY OF ASSOCIATION OF ATTRIBUTES IN STATISTICS , 1903 .
[53] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[54] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[55] Mingzhou Ding,et al. The dynamic brain : an exploration of neuronal variability and its functional significance , 2011 .
[56] Olivier Pourret,et al. Bayesian networks : a practical guide to applications , 2008 .
[57] Eugene Santos,et al. Case-Based Bayesian Network Classifiers , 2004, FLAIRS.
[58] Nir Friedman,et al. Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks , 2004, Machine Learning.
[59] Javier DeFelipe,et al. Cortical interneurons: from Cajal to 2001. , 2002, Progress in brain research.
[60] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[61] Steffen L. Lauritzen,et al. Bayesian updating in causal probabilistic networks by local computations , 1990 .
[62] Judea Pearl,et al. Probabilistic reasoning in intelligent systems , 1988 .
[63] Alan Peters,et al. Cellular components of the cerebral cortex , 1984 .
[64] David G. Stork,et al. Pattern Classification , 1973 .
[65] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .