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List of AISTATS-97 Accepted Papers

• Intelligent Support of Secondary Data Analysis
  Russell G. Almond
• Graphical Model Based Computer Adaptive Testing
  Russell G. Almond & Robert J. Mislevy
• Finding Overlapping Distributions with MML
  Rohan A. Baxter & Jonathan J. Oliver
• Markov chain Monte Carlo methods for decision analysis
  Concha Bielza, Peter Muller, & David Rios Insua
• A Comparison of decision trees, influence diagrams, & valuation networks for Asymmetric Decision Problems
  Concha Bielza & Prakash P.Shenoy
• Integrating Signal and Language Context to Improve Handwritten Phrase Recognition: Alternative Approaches
  Djamel Bouchaffra, Eugene Koontz, V. Krpasundar, Rohini K. Srihari, & Sargur N. Srihari,
• Using Prediction to Improve Combinatorial Optimization Search
  Justin A. Boyan & Andrew W. Moore
• Comparing Tree-Simplification Procedures
  Leonard A. Breslow & David W. Aha
• A Forward Monte Carlo Method for Solving Influence Diagrams using local Computation
  John M. Charnes & Prakash P. Shenoy
• An algorithm for Bayesian network construction from data
  Jie Cheng, David A. Bell, & Weiru Liu
• A Bayesian approach to CART
  Hugh Chipman, Edward I. George, & Robert E. McCulloch Strategies for model mixing in generalized linear models
  Merlise Clyde
• Overfitting Explained
  Paul R. Cohen & David Jensen
• Using classification trees to improve causal inferences in observational studies
  Louis Anthony Cox
• Dataset cataloging metadata for machine learning applications research
  Sally Jo Cunningham
• PAC learning with constant-partition classification noise and applications to decision tree induction
  Scott E. Decatur
• Bayesian model averaging in rule induction
  Pedro Domingos
• Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators
  Artur Dubrawski & Jeff Schneider
• Inductive Inference of First-Order Models from Numeric-Symbolic Data
  Floriana Esposito, Sergio Caggese, Donato Malerba, & Giovanni Semeraro
• Learning Influence Diagram from Data
  Kazuo J. Ezawa & Narendra K. Gupta
• Inference using Probabilistic Concept Trees
  Doug Fisher & Doug Talbert
• A Characterization of Bayesian Network Structures and its Application to Learning
  James I.G. Forbes
• Variational inference for continuous sigmoidal belief networks
  Brendan J. Frey
• Multivariate Density Factorization for Independent Component Analysis: An Unsupervised Artificial Neural Network Approach
  Mark Girolami & Colin Fyfe
• Intelligent Assistant for Computational Scientists: Integrated modelling, experimentation, and analysis
  Dawn E. Gregory & Paul R. Cohen
• On Predictive Classification of Binary Vectors
  Mats Gyllenberg & Timo Koski
• Asessing and Improving Classification Rules
  David J. Hand, Keming Yu, & Niall Ada
• Robust interpretation of neural network models
  Orna Intrator & Nathan Intrator
• Wavelet based random densities
  David Rios Insua & Brani Vidakovic A comparison of scientific and engineering criteria for Bayesian model selection
  David Heckerman & David Maxwell Chickering
• A variational approach to Bayesian logistic regression models and their extensions
  Tommi S. Jaakkola & Michael I. Jordan
• Adjusting for Multiple Testing in Decision Tree Pruning
  David Jensen
• Bayesian Information Retrieval: Preliminary Evaluation
  Michelle Keim, David D. Lewis, & David Madigan
• Comparing predictive inference methods for discrete domains
  Petri Kontkanen, Petri Myllymaki, Tomi Silander, Henry Tirri, & Peter Grunwald Approximate Inference and Forecast Algorithms in Graphical Models for Partially Observed Dynamic Systems
  Alberto Lekuona, Beatrix Lacruz, & Pilar Lasala
• Unsupervised clustering with numeric and nominal mixed data - a new similarity based system
  Cen Li & Gautam Biswas
• How to Find Big-OH in Your Data Set (and How Not To)
  C. C. McGeoch & P. R. Cohen
• An objective function for belief net triangulation
  Marina Meila & Michael I. Jordan
• Combining neural network regression estimates using principal components
  Christopher J. Merz & Michael J. Pazzani
• A family of algorithms for finding temporal structure in data
  Tim Oates, Matthew J. Schmill, David Jensen, & Paul R. Cohen
• The Effects of Training Set Size on Decision Tree Complexity
  Tim Oates & David Jensen
• Case-based Probability Factoring in Bayesian Belief Networks
  Luigi Portinale
• Robust parameter learning in Bayesian networks with missing data
  Marco Ramoni & Paola Sebastiani
• Extensions of undirected and acyclic, directed graphical models
  Thomas Richardson
• A note on cyclic graphs and dynamical feedback systems
  Thomas Richardson, Peter Spirtes, & Clark Glymour
• Applying a Gaussian-Bernoulli Mixture Model Network to Binary and Continuous Missing Data in Medicine
  David B. Rosen & Harry B. Burke
• Mixed memory Markov models
  Lawrence K. Saul & Michael I. Jordan
• Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation
  Richard Scheines
• A Distance Metric for Classification Trees
  William D. Shannon & David Banks
• An Incremental Construction of a Nonparametric Regression Models
  Jan Smid and Petr Volf
• Cross-validated likelihood for model selection in unsupervised learning
  Padhraic Smyth
• Heuristic greedy search algorithms for latent variable models
  Peter Spirtes, Thomas Richardson, & Christopher Meek
• A polynomial time algorithm for determining DAG equivalence in the presence of latent variables and selection bias
  Peter Spirtes & Thomas Richardson
• Building an EDA Assistant: A Progress Report
  Robert St. Amant & Paul R. Cohen
• On the error probability of model selection for classification
  Joe Suzuki
• Statistical Aspects of Classification in Drifting Populations
  C.C. Taylor, G. Nakhaeizadeh, & G. Kunisch
• MML Mixture modelling of multi-state, Poisson, vonMises circular, & Gaussian distributions
  Chris S. Wallace & David L. Dowe
• WWW Cache Layout to Ease Network Overload
  Kenichi Yoshida