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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