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ORCID8th AISTATS 2001: Key West, Florida, USA
- Thomas S. Richardson, Tommi S. Jaakkola:
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, AISTATS 2001, Key West, Florida, USA, January 4-7, 2001. Society for Artificial Intelligence and Statistics 2001 - Russell G. Almond, Lou DiBello, Frank Jenkins, Deniz Senturk, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan:
Models for Conditional Probability Tables in Educational Assessment. 1-7 - Hagai Attias:
Learning in high dimensions: modular mixture models. 8-12 - Susanne Bottcher:
Learning Bayesian networks with mixed variables. 13-20 - Andrew D. Brown, Geoffrey E. Hinton:
Products of Hidden Markov Models. 21-28 - Ariel E. Bud, David W. Albrecht, Ann E. Nicholson, Ingrid Zukerman:
Information-Theoretic Advisors in Invisible Chess. 29-34 - Rich Caruana:
A Non-Parametric EM-Style Algorithm for Imputing Missing Values. 35-40 - Hugh A. Chipman, Edward I. George, Robert E. McCulloch:
Managing Multiple Models. 41-48 - Samuel Ping-Man Choi, Nevin Lianwen Zhang, Dit-Yan Yeung:
Solving Hidden-Mode Markov Decision Problems. 49-56 - Merlise A. Clyde, Herbert K. H. Lee:
Bagging and the Bayesian Bootstrap. 57-62 - Adrian Corduneanu, Christopher M. Bishop:
Hyperparameters for Soft Bayesian Model Selection. 63-70 - Robert G. Cowell:
On searching for optimal classifiers among Bayesian networks. 71-76 - James Cussens:
Statistical Aspects of Stochastic Logic Programs. 77-82 - A. Philip Dawid:
Some variations on variation independence.. 83-86 - Eric de Bodt, Marie Cottrell, Michel Verleysen:
Are they really neighbors? A statistical analysis of the SOM algorithm output. 87-92 - Michael O. Duff:
Monte-Carlo Algorithms for the Improvement of Finite-State Stochastic Controllers: Application to Bayes-Adaptive Markov Decision Processes. 93-97 - Yoav Freund, Yishay Mansour, Robert E. Schapire:
Why averaging classifiers can protect against overfitting. 98-105 - Pierre Geurts:
Dual perturb and combine algorithm. 106-111 - Phil D. Green, Jon Barker, Martin Cooke, Ljubomir Josifovski:
Handling Missing and Unreliable Information in Speech Recognition. 112-116 - Anne Guérin-Dugué, Gilles Celeux:
Discriminant Analysis on Dissimilarity Data : a New Fast Gaussian like Algorithm. 117-122 - Malene Højbjerre:
Profile Likelihood in Directed Graphical Models from BUGS Output. 123-128 - Wenxin Jiang:
Is regularization unnecessary for boosting?. 129-136 - Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman:
Learning mixtures of smooth, nonuniform deformation models for probabilistic image matching. 137-142 - Mehmet Kayaalp, Gregory F. Cooper, Gilles Clermont:
Predicting with Variables Constructed from Temporal Sequences. 143-148 - Oscar Kipersztok, Haiqin Wang:
Another look at sensitivity of Bayesian networks to imprecise probabilities. 149-155 - Petri Kontkanen, Petri Myllymäki, Henry Tirri:
Comparing Prequential Model Selection Criteria in Supervised Learning of Mixture Models. 156-161 - Martin H. C. Law, James Tin-Yau Kwok:
Bayesian Support Vector Regression. 162-167 - Neil D. Lawrence:
Variational Learning for Multi-Layer Networks of Linear Threshold Units. 168-175 - Lillian Lee
:
On the effectiveness of the skew divergence for statistical language analysis. 176-183 - Subramani Mani, Gregory F. Cooper:
A Simulation Study of Three Related Causal Data Mining Algorithms. 184-191 - Christopher Meek:
Finding a path is harder than finding a tree. 192-195 - Christopher Meek, Bo Thiesson, David Heckerman:
The Learning Curve Method Applied to Clustering. 196-202 - Marina Meila, Jianbo Shi:
A Random Walks View of Spectral Segmentation. 203-208 - Sebastian Mika, Alexander J. Smola, Bernhard Schölkopf:
An improved training algorithm for kernel Fisher discriminants. 209-215 - Scott Needham, David L. Dowe:
Message Length as an Effective Ockham's Razor in Decision Tree Induction. 216-223 - Adam Nickerson, Nathalie Japkowicz, Evangelos E. Milios:
Using Unsupervised Learning to Guide Resampling in Imbalanced Data Sets. 224-228 - Nikunj C. Oza, Stuart Russell:
Online Bagging and Boosting. 229-236 - José M. Peña, I. Izarzugaza, José Antonio Lozano, E. Aldasoro, Pedro Larrañaga:
Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks. 237-242 - Gregory M. Provan:
Stochastic System Monitoring and Control. 243-250 - Christopher Raphael:
Can the Computer Learn to Play Music Expressively?. 251-258 - Dmitry Rusakov, Dan Geiger:
On Parameter Priors for Discrete DAG Models. 259-264 - Richard Scheines, Gregory F. Cooper, Changwon Yoo, Tianjiao Chu:
Piecewise Linear Instrumental Variable Estimation of Causal Influence. 265-271 - Duncan Smith:
The Efficient Propagation of Arbitrary Subsets of Beliefs in Discrete-Valued Bayesian Networks. 272-277 - Peter Spirtes:
An Anytime Algorithm for Causal Inference. 278-285 - Amos J. Storkey, Christopher K. I. Williams:
Dynamic Positional Trees for Structural Image Analysis. 286-292 - Ahmed Y. Tawfik, Greg Scott:
Temporal Matching under Uncertainty. 293-297 - Michael E. Tipping, Bernhard Schölkopf:
A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds. 298-303
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