Table of Contents

• Models for Conditional Probability Tables in Educational AssessmentRussell G. Almond, Lou DiBello, Frank Jenkins, Deniz Senturk, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan. 1-7 [doi]
• Learning in high dimensions: modular mixture modelsHagai Attias. 8-12 [doi]
• Learning Bayesian networks with mixed variablesSusanne Bottcher. 13-20 [doi]
• Products of Hidden Markov ModelsAndrew D. Brown, Geoffrey E. Hinton. 21-28 [doi]
• Information-Theoretic Advisors in Invisible ChessAriel E. Bud, David W. Albrecht, Ann E. Nicholson, Ingrid Zukerman. 29-34 [doi]
• A Non-Parametric EM-Style Algorithm for Imputing Missing ValuesRich Caruana. 35-40 [doi]
• Managing Multiple ModelsHugh A. Chipman, Edward I. George, Robert E. McCulloch. 41-48 [doi]
• Solving Hidden-Mode Markov Decision ProblemsSamuel Ping-Man Choi, Nevin Lianwen Zhang, Dit-Yan Yeung. 49-56 [doi]
• Bagging and the Bayesian BootstrapMerlise Clyde, Herbert Lee. 57-62 [doi]
• Hyperparameters for Soft Bayesian Model SelectionAdrian Corduneanu, Christopher M. Bishop. 63-70 [doi]
• On searching for optimal classifiers among Bayesian networksRobert G. Cowell. 71-76 [doi]
• Statistical Aspects of Stochastic Logic ProgramsJames Cussens. 77-82 [doi]
• Some variations on variation independence.A. P. Dawid. 83-86 [doi]
• Are they really neighbors? A statistical analysis of the SOM algorithm outputEric de Bodt, Marie Cottrell, Michel Verleysen. 87-92 [doi]
• Monte-Carlo Algorithms for the Improvement of Finite-State Stochastic Controllers: Application to Bayes-Adaptive Markov Decision ProcessesMichael O. Duff. 93-97 [doi]
• Why averaging classifiers can protect against overfittingYoav Freund, Yishay Mansour, Robert E. Schapire. 98-105 [doi]
• Dual perturb and combine algorithmPierre Geurts. 106-111 [doi]
• Handling Missing and Unreliable Information in Speech RecognitionPhil D. Green, Jon Barker, Martin Cooke, Ljubomir Josifovski. 112-116 [doi]
• Discriminant Analysis on Dissimilarity Data : a New Fast Gaussian like AlgorithmAnne Guérin-Dugué, Gilles Celeux. 117-122 [doi]
• Profile Likelihood in Directed Graphical Models from BUGS OutputMalene Højbjerre. 123-128 [doi]
• Is regularization unnecessary for boosting?Wenxin Jiang. 129-136 [doi]
• Learning mixtures of smooth, nonuniform deformation models for probabilistic image matchingNebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman. 137-142 [doi]
• Predicting with Variables Constructed from Temporal SequencesMehmet Kayaalp, Gregory F. Cooper, Gilles Clermont. 143-148 [doi]
• Another look at sensitivity of Bayesian networks to imprecise probabilitiesOscar Kipersztok, Haiqin Wang. 149-155 [doi]
• Comparing Prequential Model Selection Criteria in Supervised Learning of Mixture ModelsPetri Kontkanen, Petri Myllymäki, Henry Tirri. 156-161 [doi]
• Bayesian Support Vector RegressionMartin H. C. Law, James Tin-Yau Kwok. 162-167 [doi]
• Variational Learning for Multi-Layer Networks of Linear Threshold UnitsNeil D. Lawrence. 168-175 [doi]
• On the effectiveness of the skew divergence for statistical language analysisLillian Lee. 176-183 [doi]
• A Simulation Study of Three Related Causal Data Mining AlgorithmsSubramani Mani, Gregory F. Cooper. 184-191 [doi]
• Finding a path is harder than finding a treeChristopher Meek. 192-195 [doi]
• The Learning Curve Method Applied to ClusteringChristopher Meek, Bo Thiesson, David Heckerman. 196-202 [doi]
• A Random Walks View of Spectral SegmentationMarina Meila, Jianbo Shi. 203-208 [doi]
• An improved training algorithm for kernel Fisher discriminantsSebastian Mika, Alexander J. Smola, Bernhard Schölkopf. 209-215 [doi]
• Message Length as an Effective Ockham's Razor in Decision Tree InductionScott Needham, David L. Dowe. 216-223 [doi]
• Using Unsupervised Learning to Guide Resampling in Imbalanced Data SetsAdam Nickerson, Nathalie Japkowicz, Evangelos E. Milios. 224-228 [doi]
• Online Bagging and BoostingNikunj C. Oza, Stuart J. Russell. 229-236 [doi]
• Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networksJosé Manuel Peña, I. Izarzugaza, José Antonio Lozano, E. Aldasoro, Pedro Larrañaga. 237-242 [doi]
• Stochastic System Monitoring and ControlGregory M. Provan. 243-250 [doi]
• Can the Computer Learn to Play Music Expressively?Christopher Raphael. 251-258 [doi]
• On Parameter Priors for Discrete DAG ModelsDmitry Rusakov, Dan Geiger. 259-264 [doi]
• Piecewise Linear Instrumental Variable Estimation of Causal InfluenceRichard Scheines, Gregory F. Cooper, Changwon Yoo, Tianjiao Chu. 265-271 [doi]
• The Efficient Propagation of Arbitrary Subsets of Beliefs in Discrete-Valued Bayesian NetworksDuncan Smith. 272-277 [doi]
• An Anytime Algorithm for Causal InferencePeter Spirtes. 278-285 [doi]
• Dynamic Positional Trees for Structural Image AnalysisAmos J. Storkey, Christopher K. I. Williams. 286-292 [doi]
• Temporal Matching under UncertaintyAhmed Y. Tawfik, Greg Scott. 293-297 [doi]
• A Kernel Approach for Vector Quantization with Guaranteed Distortion BoundsMichael E. Tipping, Bernhard Schölkopf. 298-303 [doi]