Table of Contents

• A Bayesian Approach to Bergman's Minimal ModelKim E. Andersen, Malene Højbjerre. 1-8 [doi]
• Planning by Probabilistic InferenceHagai Attias. 9-16 [doi]
• Quick Training of Probabilistic Neural Nets by Importance SamplingYoshua Bengio, Jean-Sébastien Senecal. 17-24 [doi]
• Super-resolution Enhancement of VideoChristopher M. Bishop, Andrew Blake, Bhaskara Marthi. 25-32 [doi]
• Structured Variational Distributions in VIBESChristopher M. Bishop, John M. Winn. 33-40 [doi]
• A unifying theorem for spectral embedding and clusteringMatthew Brand, Kun Huang. 41-48 [doi]
• The Sound of an Album Cover: A Probabilistic Approach to MultimediaEric Brochu, Nando de Freitas, Kejie Bao. 49-56 [doi]
• Is Multinomial PCA Multi-faceted Clustering or Dimensionality Reduction?Wray L. Buntine, Sami Perttu. 57-64 [doi]
• Expectation Maximization of Forward Decoding Kernel MachinesShantanu Chakrabartty, Gert Cauwenberghs. 65-71 [doi]
• Model Averaging with Bayesian Network ClassifiersDenver Dash, Gregory F. Cooper. 72-79 [doi]
• An object-oriented Bayesian network for estimating mutation ratesA. Philip Dawid. 80-84 [doi]
• Document Retrieval and Clustering: from Principal Component Analysis to Self-aggregation NetworksChris Ding. 85-92 [doi]
• On the Naive Bayes Model for Text CategorizationSusana Eyheramendy, David D. Lewis, David Madigan. 93-100 [doi]
• Curve Clustering with Random Effects Regression MixturesScott Gaffney, Padhraic Smyth. 101-108 [doi]
• Clustering Markov States into Equivalence Classes using SVD and Heuristic Search AlgorithmsXianping Ge, Sridevi Parise, Padhraic Smyth. 109-116 [doi]
• Rapid Evaluation of Multiple Density ModelsAlexander G. Gray, Andrew W. Moore. 117-123 [doi]
• Bayesian Feature Weighting for Unsupervised Learning, with Application to Object RecognitionPaul Gustafson, Peter Carbonetto, Natalie Thompson, Nando de Freitas. 124-131 [doi]
• Generalized belief propagation for approximate inference in hybrid Bayesian networksTom Heskes, Onno Zoeter. 132-140 [doi]
• Learning Bayesian Networks From Dependency Networks: A Preliminary StudyGeoff Hulten, David Maxwell Chickering, David Heckerman. 141-148 [doi]
• Convex Invariance LearningTony Jebara. 149-156 [doi]
• Refining Kernels for Regression and Uneven Classification ProblemsJaz S. Kandola, John Shawe-Taylor. 157-162 [doi]
• Fast Robust Logistic Regression for Large Sparse Datasets with Binary OutputsPaul Komarek, Andrew W. Moore. 163-170 [doi]
• Efficient Computing of Stochastic ComplexityPetri Kontkanen, Wray L. Buntine, Petri Myllymäki, Jorma Rissanen, Henry Tirri. 171-178 [doi]
• The Joint Causal Effect in Linear Structural Equation Model and Its Application to Process AnalysisManabu Kuroki, Zhihong Cai. 179-186 [doi]
• Bayesian Inference in the Presence of DeterminismDavid Larkin, Rina Dechter. 187-194 [doi]
• Reduced Rank Approximations of Transition MatricesJuan Lin. 195-202 [doi]
• On Retrieval Properties of Samples of Large CollectionsDavid Madigan, Yehuda Vardi, Ishay Weissman. 203-208 [doi]
• Data centering in feature spaceMarina Meila. 209-216 [doi]
• A Blessing of Dimensionality: Measure Concentration and Probabilistic InferencePinar Muyan, Nando de Freitas. 217-224 [doi]
• Real-time On-line Learning of Transformed Hidden Markov Models from VideoNemanja Petrovic, Nebojsa Jojic, Brendan J. Frey, Thomas S. Huang. 225-232 [doi]
• Ensemble Coupled Hidden Markov Models for Joint Characterisation of Dynamic SignalsIead Rezek, Stephen J. Roberts, Peter Sykacek. 233-239 [doi]
• A Generalized Linear Model for Principal Component Analysis of Binary DataAndrew I. Schein, Lawrence K. Saul, Lyle H. Ungar. 240-247 [doi]
• Combining Conjugate Direction Methods with Stochastic Approximation of GradientsNicol N. Schraudolph, Thore Graepel. 248-253 [doi]
• Fast Forward Selection to Speed Up Sparse Gaussian Process RegressionMatthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence. 254-261 [doi]
• On Improving the Efficiency of the Iterative Proportional Fitting ProcedureYee Whye Teh, Max Welling. 262-269 [doi]
• Discriminative Model Selection for Density ModelsBo Thiesson, Christopher Meek. 270-275 [doi]
• Fast Marginal Likelihood Maximisation for Sparse Bayesian ModelsMichael E. Tipping, Anita C. Faul. 276-283 [doi]
• Sequential Importance Sampling for Visual Tracking ReconsideredPéter Torma, Csaba Szepesvári. 284-291 [doi]
• Solving Markov Random Fields using Semi Definite ProgrammingPhilip H. S. Torr. 292-299 [doi]
• Towards Principled Feature Selection: Relevancy, Filters and WrappersIoannis Tsamardinos, Constantin F. Aliferis. 300-307 [doi]
• Tree-reweighted belief propagation algorithms and approximate ML estimation by pseudo-moment matchingMartin J. Wainwright, Tommi Jaakkola, Alan S. Willsky. 308-315 [doi]
• Latent Maximum Entropy Approach for Semantic N-gram Language ModelingShaojun Wang, Dale Schuurmans, Fuchun Peng. 316-322 [doi]
• On Boosting and the Exponential LossAbraham J. Wyner. 323-329 [doi]
• An Active Approach to Collaborative FilteringRichard S. Zemel, Craig Boutilier. 330-337 [doi]