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ORCID6th AISTATS 1997: Fort Lauderdale, FL, USA
- David Madigan, Padhraic Smyth:
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, AISTATS 1997, Fort Lauderdale, Florida, USA, January, 4-7, 1997. MLR Press 1997 - David Madigan, Padhraic Smyth:
Preface. i-xiii - Russell G. Almond:
Intelligent Support of Secondary Data Analysis. 1-10 - Russell G. Almond, Robert J. Mislevy:
Graphical Model Based Computer Adaptive Testing. 11-22 - Rohan A. Baxter, Jonathan J. Oliver:
Finding Overlapping Distributions with MML. 23-30 - Concha Bielza, Peter Müller, David Ríos Insua:
Markov chain Monte Carlo methods for decision analysis. 31-38 - Concha Bielza, Prakash P. Shenoy:
A Comparison of Decision Trees, Influence Diagrams and Valuation Networks for Asymmetric Decision Problems. 39-46 - Djamel Bouchaffra, Eugene Koontz, Venu Krpasundar, Rohini K. Srihari, Sargur N. Srihari:
Integrating Signal and Language Context to Improve Handwritten Phrase Recognition: Alternative Approaches. 47-54 - Justin A. Boyan, Andrew W. Moore:
Using Prediction to Improve Combinatorial Optimization Search. 55-66 - Leonard A. Breslow, David W. Aha:
Comparing Tree-Simplification Procedures. 67-74 - John M. Charnes, Prakash P. Shenoy:
A Forward Monte Carlo Method for Solving Influence Diagrams using local Computation. 75-82 - Jie Cheng, David A. Bell, Weiru Liu:
An Algorithm for Bayesian Network Construction from Data. 83-90 - Hugh A. Chipman, Edward I. George, Robert E. McCulloch:
A Bayesian approach to CART. 91-102 - Merlise A. Clyde:
Strategies for Model Mixing in Generalized Linear Models. 103-114 - Paul R. Cohen, David D. Jensen:
Overfitting Explained. 115-122 - Louis Anthony Cox Jr.:
Using Classification Trees to Improve Causal Inferences in Observational Studies. 123-138 - Sally Jo Cunningham:
Dataset Cataloging Metadata for Machine Learning Applications Research. 139-146 - Scott E. Decatur:
PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction. 147-156 - Pedro Domingos:
Bayesian Model Averaging in Rule Induction. 157-164 - Artur Dubrawski, Jeff Schneider:
Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators. 165-172 - Floriana Esposito, Sergio Caggese, Donato Malerba, Giovanni Semeraro:
Inductive Inference of First-Order Models from Numeric-Symbolic Data. 173-182 - Kazuo J. Ezawa, Narendra K. Gupta:
Leaming Influence Diagram from Data. 183-190 - Douglas H. Fisher, Douglas A. Talbert:
Inference using Probabilistic Concept Trees. 191-202 - James I. G. Forbes:
A Characterization of Bayesian Network Structures and its Application to Leaming. 203-210 - Brendan J. Frey:
Variational Inference for continuous Sigmoidal Bayesian Networks. 211-222 - Mark Girolami, Colin Fyfe:
Multivariate Density Factorization for Independent Component Analysis: An Unsupervised Artificial Neural Network Approach. 223-230 - Dawn E. Gregory, Paul R. Cohen:
Intelligent Assistant for Computational Scientists: Integrated Modelling, Experimentation and Analysis. 231-238 - Mats Gyllenberg, Timo Koski:
On Predictive Classification of Binary Vectors. 239-242 - David J. Hand, Keming Yu, Niall M. Adams:
Asessing and Improving Classification Rules. 243-254 - Orna Intrator, Nathan Intrator:
Robust Interpretation of Neural Network models. 255-262 - David Ríos Insua, Brani Vidakovic:
Wavelet based Random Densities. 263-274 - David Heckerman, David Maxwell Chickering:
A Comparison of Scientific and Engineering Criteria for Bayesian Model Selection. 275-282 - Tommi S. Jaakkola, Michael I. Jordan:
A Variational Approach to Bayesian Logistic Regression Models and their Extensions. 283-294 - David D. Jensen:
Adjusting for Multiple Testing in Decision Tree Pruning. 295-302 - Michelle Keim, David D. Lewis, David Madigan:
Bayesian Information Retrieval: Preliminary Evaluation. 303-318 - Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri, Peter Grünwald:
Comparing Predictive Inference Methods for Discrete Domains. 311-318 - Alberto Lekuona, Beatriz Lacruz, Pilar Lasala:
Approximate Inference and Forecast Algorithms in Graphical Models for Partially Observed Dynamic Systems. 319 - Cen Li, Gautam Biswas:
Conceptual Clustering with Numeric-and-Nominal Mixed Data - A New Similarity Based System. 327-346 - Catherine C. McGeoch, Paul R. Cohen:
How to Find Big-Oh in Your Data Set (and How Not To). 347-354 - Marina Meila, Michael I. Jordan:
An Objective Function for Belief Net Triangulation. 355-362 - Christopher J. Merz, Michael J. Pazzani:
Combining Neural Network Regression Estimates Using Principal Components. 363-370 - Tim Oates, Matthew D. Schmill, David D. Jensen, Paul R. Cohen:
A Family of Algorithms for Finding Temporal Structure in Data. 371-378 - Tim Oates, David D. Jensen:
The Effects of Training Set Size on Decision Tree Complexity. 379-390 - Luigi Portinale:
Case-based Probability Factoring in Bayesian Belief Networks. 391-398 - Marco Ramoni, Paola Sebastiani:
Robust Parameter Learning in Bayesian Networks with Missing Data. 399-406 - Thomas S. Richardson:
Extensions of Undirected and Acyclic, Directed Graphical Models. 407-420 - Thomas S. Richardson, Peter Spirtes, Clark Glymour:
A Note on Cyclic Graphs and Dynamical Feedback Systems. 421-428 - David B. Rosen, Harry B. Burke:
Applying a Gaussian-Bernoulli Mixture Model Network to Binary and Continuous Missing Data in Medicine. 429-436 - Lawrence K. Saul, Michael I. Jordan:
Mixed Memory Markov Models. 437-444 - Richard Scheines:
Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation. 445-456 - William D. Shannon, David Banks:
A Distance Metric for Classification Trees. 457-464 - Jan Smid, Petr Volf:
An Incremental Construction of a Nonparametric Regression Model. 465-472 - Padhraic Smyth:
Cross-validated Likelihood for Model Selection in Unsupervised Learning. 473-480 - Peter Spirtes, Thomas S. Richardson, Christopher Meek:
Heuristic Greedy Search Algorithms for Latent Variable Models. 481-488 - Peter Spirtes, Thomas S. Richardson:
A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias. 489-500 - Robert St. Amant, Paul R. Cohen:
Building an EDA Assistant: A Progress Report. 501-512 - Joe Suzuki:
On the Error Probability of Model Selection for Classification. 513-520 - Charles C. Taylor, Gholamreza Nakhaeizadeh, G. Kunisch:
Statistical Aspects of Classification in Drifting Populations. 521-528 - Chris S. Wallace, David L. Dowe:
MML Mixture Modelling of Multi-state, Poisson, vonMises circular and Gaussian Distributions. 529-536 - Kenichi Yoshida:
WWW Cache Layout to Ease Network Overload. 537-548
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