Table of Contents

• PrefaceDavid Madigan, Padhraic Smyth. [doi]
• Intelligent Support of Secondary Data AnalysisRussell G. Almond. 1-10 [doi]
• Graphical Model Based Computer Adaptive TestingRussell G. Almond, Robert J. Mislevy. 11-22 [doi]
• Finding Overlapping Distributions with MMLRohan A. Baxter, Jonathan J. Oliver. 23-30 [doi]
• Markov chain Monte Carlo methods for decision analysisConcha Bielza, Peter Müller 0003, David Ríos Insua. 31-38 [doi]
• A Comparison of Decision Trees, Influence Diagrams and Valuation Networks for Asymmetric Decision ProblemsConcha Bielza, Prakash P. Shenoy. 39-46 [doi]
• Integrating Signal and Language Context to Improve Handwritten Phrase Recognition: Alternative ApproachesDjamel Bouchaffra, Eugene Koontz, Venu Krpasundar, Rohini K. Srihari, Sargur N. Srihari. 47-54 [doi]
• Using Prediction to Improve Combinatorial Optimization SearchJustin A. Boyan, Andrew W. Moore 0001. 55-66 [doi]
• Comparing Tree-Simplification ProceduresLeonard A. Breslow, David W. Aha. 67-74 [doi]
• A Forward Monte Carlo Method for Solving Influence Diagrams using local ComputationJohn M. Charnes, Prakash P. Shenoy. 75-82 [doi]
• An Algorithm for Bayesian Network Construction from DataJie Cheng, David A. Bell, Weiru Liu. 83-90 [doi]
• A Bayesian approach to CARTHugh A. Chipman, Edward I. George, Robert E. McCulloch. 91-102 [doi]
• Strategies for Model Mixing in Generalized Linear ModelsMerlise A. Clyde. 103-114 [doi]
• Overfitting ExplainedPaul R. Cohen, David D. Jensen. 115-122 [doi]
• Using Classification Trees to Improve Causal Inferences in Observational StudiesLouis Anthony Cox Jr.. 123-138 [doi]
• Dataset Cataloging Metadata for Machine Learning Applications ResearchSally Jo Cunningham. 139-146 [doi]
• PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree InductionScott E. Decatur. 147-156 [doi]
• Bayesian Model Averaging in Rule InductionPedro Domingos. 157-164 [doi]
• Memory Based Stochastic Optimization for Validation and Tuning of Function ApproximatorsArtur Dubrawski, Jeff Schneider 0001. 165-172 [doi]
• Inductive Inference of First-Order Models from Numeric-Symbolic DataFloriana Esposito, Sergio Caggese, Donato Malerba, Giovanni Semeraro. 173-182 [doi]
• Leaming Influence Diagram from DataKazuo J. Ezawa, Narendra K. Gupta. 183-190 [doi]
• Inference using Probabilistic Concept TreesDouglas H. Fisher, Douglas A. Talbert. 191-202 [doi]
• A Characterization of Bayesian Network Structures and its Application to LeamingJames I. G. Forbes. 203-210 [doi]
• Variational Inference for continuous Sigmoidal Bayesian NetworksBrendan J. Frey. 211-222 [doi]
• Multivariate Density Factorization for Independent Component Analysis: An Unsupervised Artificial Neural Network ApproachMark Girolami, Colin Fyfe. 223-230 [doi]
• Intelligent Assistant for Computational Scientists: Integrated Modelling, Experimentation and AnalysisDawn E. Gregory, Paul R. Cohen. 231-238 [doi]
• On Predictive Classification of Binary VectorsMats Gyllenberg, Timo Koski. 239-242 [doi]
• Asessing and Improving Classification RulesDavid J. Hand, Keming Yu, Niall M. Adams. 243-254 [doi]
• Robust Interpretation of Neural Network modelsOrna Intrator, Nathan Intrator. 255-262 [doi]
• Wavelet based Random DensitiesDavid Ríos Insua, Brani Vidakovic. 263-274 [doi]
• A Comparison of Scientific and Engineering Criteria for Bayesian Model SelectionDavid Heckerman, David Maxwell Chickering. 275-282 [doi]
• A Variational Approach to Bayesian Logistic Regression Models and their ExtensionsTommi S. Jaakkola, Michael I. Jordan. 283-294 [doi]
• Adjusting for Multiple Testing in Decision Tree PruningDavid D. Jensen. 295-302 [doi]
• Bayesian Information Retrieval: Preliminary EvaluationMichelle Keim, David D. Lewis, David Madigan. 303-318 [doi]
• Comparing Predictive Inference Methods for Discrete DomainsPetri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald. 311-318 [doi]
• Approximate Inference and Forecast Algorithms in Graphical Models for Partially Observed Dynamic SystemsAlberto Lekuona, Beatriz Lacruz, Pilar Lasala. 319 [doi]
• Conceptual Clustering with Numeric-and-Nominal Mixed Data - A New Similarity Based SystemCen Li, Gautam Biswas. 327-346 [doi]
• How to Find Big-Oh in Your Data Set (and How Not To)Catherine C. McGeoch, Paul R. Cohen. 347-354 [doi]
• An Objective Function for Belief Net TriangulationMarina Meila, Michael I. Jordan. 355-362 [doi]
• Combining Neural Network Regression Estimates Using Principal ComponentsChristopher J. Merz, Michael J. Pazzani. 363-370 [doi]
• A Family of Algorithms for Finding Temporal Structure in DataTim Oates 0001, Matthew D. Schmill, David D. Jensen, Paul R. Cohen. 371-378 [doi]
• The Effects of Training Set Size on Decision Tree ComplexityTim Oates 0001, David D. Jensen. 379-390 [doi]
• Case-based Probability Factoring in Bayesian Belief NetworksLuigi Portinale. 391-398 [doi]
• Robust Parameter Learning in Bayesian Networks with Missing DataMarco Ramoni, Paola Sebastiani. 399-406 [doi]
• Extensions of Undirected and Acyclic, Directed Graphical ModelsThomas S. Richardson 0001. 407-420 [doi]
• A Note on Cyclic Graphs and Dynamical Feedback SystemsThomas S. Richardson 0001, Peter Spirtes, Clark Glymour. 421-428 [doi]
• Applying a Gaussian-Bernoulli Mixture Model Network to Binary and Continuous Missing Data in MedicineDavid B. Rosen, Harry B. Burke. 429-436 [doi]
• Mixed Memory Markov ModelsLawrence K. Saul, Michael I. Jordan. 437-444 [doi]
• Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimationRichard Scheines. 445-456 [doi]
• A Distance Metric for Classification TreesWilliam D. Shannon, David Banks. 457-464 [doi]
• An Incremental Construction of a Nonparametric Regression ModelJan Smid, Petr Volf. 465-472 [doi]
• Cross-validated Likelihood for Model Selection in Unsupervised LearningPadhraic Smyth. 473-480 [doi]
• Heuristic Greedy Search Algorithms for Latent Variable ModelsPeter Spirtes, Thomas S. Richardson 0001, Christopher Meek. 481-488 [doi]
• A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection BiasPeter Spirtes, Thomas S. Richardson 0001. 489-500 [doi]
• Building an EDA Assistant: A Progress ReportRobert St. Amant, Paul R. Cohen. 501-512 [doi]
• On the Error Probability of Model Selection for ClassificationJoe Suzuki. 513-520 [doi]
• Statistical Aspects of Classification in Drifting PopulationsCharles C. Taylor, Gholamreza Nakhaeizadeh, G. Kunisch. 521-528 [doi]
• MML Mixture Modelling of Multi-state, Poisson, vonMises circular and Gaussian DistributionsChris S. Wallace, David L. Dowe. 529-536 [doi]
• WWW Cache Layout to Ease Network OverloadKenichi Yoshida. 537-548 [doi]