Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID8. 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. - Hagai Attias:
Learning in high dimensions: modular mixture models. - Susanne Bottcher:
Learning Bayesian networks with mixed variables. - Andrew D. Brown, Geoffrey E. Hinton:
Products of Hidden Markov Models. - Ariel E. Bud, David W. Albrecht, Ann E. Nicholson, Ingrid Zukerman:
Information-Theoretic Advisors in Invisible Chess. - Rich Caruana:
A Non-Parametric EM-Style Algorithm for Imputing Missing Values. - Hugh A. Chipman, Edward I. George, Robert E. McCulloch:
Managing Multiple Models. - Samuel Ping-Man Choi, Nevin Lianwen Zhang, Dit-Yan Yeung:
Solving Hidden-Mode Markov Decision Problems. - Merlise Clyde, Herbert Lee:
Bagging and the Bayesian Bootstrap. - Adrian Corduneanu, Christopher M. Bishop:
Hyperparameters for Soft Bayesian Model Selection. - Robert G. Cowell:
On searching for optimal classifiers among Bayesian networks. - James Cussens:
Statistical Aspects of Stochastic Logic Programs. - A. Philip Dawid:
Some variations on variation independence.. - Eric de Bodt, Marie Cottrell, Michel Verleysen:
Are they really neighbors? A statistical analysis of the SOM algorithm output. - Michael O. Duff:
Monte-Carlo Algorithms for the Improvement of Finite-State Stochastic Controllers: Application to Bayes-Adaptive Markov Decision Processes. - Yoav Freund, Yishay Mansour, Robert E. Schapire:
Why averaging classifiers can protect against overfitting. - Pierre Geurts:
Dual perturb and combine algorithm. - Phil D. Green, Jon Barker, Martin Cooke, Ljubomir Josifovski:
Handling Missing and Unreliable Information in Speech Recognition. - Anne Guérin-Dugué, Gilles Celeux:
Discriminant Analysis on Dissimilarity Data : a New Fast Gaussian like Algorithm. - Malene Højbjerre:
Profile Likelihood in Directed Graphical Models from BUGS Output. - Wenxin Jiang:
Is regularization unnecessary for boosting?. - Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman:
Learning mixtures of smooth, nonuniform deformation models for probabilistic image matching. - Mehmet Kayaalp, Gregory F. Cooper, Gilles Clermont:
Predicting with Variables Constructed from Temporal Sequences. - Oscar Kipersztok, Haiqin Wang:
Another look at sensitivity of Bayesian networks to imprecise probabilities. - Petri Kontkanen, Petri Myllymäki, Henry Tirri:
Comparing Prequential Model Selection Criteria in Supervised Learning of Mixture Models. - Martin H. C. Law, James Tin-Yau Kwok:
Bayesian Support Vector Regression. - Neil D. Lawrence:
Variational Learning for Multi-Layer Networks of Linear Threshold Units. - Lillian Lee
:
On the effectiveness of the skew divergence for statistical language analysis. - Subramani Mani, Gregory F. Cooper:
A Simulation Study of Three Related Causal Data Mining Algorithms. - Christopher Meek:
Finding a path is harder than finding a tree. - Christopher Meek, Bo Thiesson, David Heckerman:
The Learning Curve Method Applied to Clustering. - Marina Meila, Jianbo Shi:
A Random Walks View of Spectral Segmentation. - Sebastian Mika, Alexander J. Smola, Bernhard Schölkopf:
An improved training algorithm for kernel Fisher discriminants. - Scott Needham, David L. Dowe:
Message Length as an Effective Ockham's Razor in Decision Tree Induction. - Adam Nickerson, Nathalie Japkowicz, Evangelos E. Milios:
Using Unsupervised Learning to Guide Resampling in Imbalanced Data Sets. - Nikunj C. Oza, Stuart Russell:
Online Bagging and Boosting. - 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. - Gregory M. Provan:
Stochastic System Monitoring and Control. - Christopher Raphael:
Can the Computer Learn to Play Music Expressively?. - Dmitry Rusakov, Dan Geiger:
On Parameter Priors for Discrete DAG Models. - Richard Scheines, Gregory F. Cooper, Changwon Yoo, Tianjiao Chu:
Piecewise Linear Instrumental Variable Estimation of Causal Influence. - Duncan Smith:
The Efficient Propagation of Arbitrary Subsets of Beliefs in Discrete-Valued Bayesian Networks. - Peter Spirtes:
An Anytime Algorithm for Causal Inference. - Amos J. Storkey, Christopher K. I. Williams:
Dynamic Positional Trees for Structural Image Analysis. - Ahmed Y. Tawfik, Greg Scott:
Temporal Matching under Uncertainty. - Michael E. Tipping, Bernhard Schölkopf:
A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds.
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-04-15 20:32 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
