MLJ 2003

44 papers

A Microchoice Bound for Continuous-Space Classification Algorithms Yoram Gat

A Noninformative Prior for Neural Networks Herbert K. H. Lee

A Theoretical Analysis of Query Selection for Collaborative Filtering Sanjoy Dasgupta, Wee Sun Lee, Philip M. Long

An Empirical Study of Two Approaches to Sequence Learning for Anomaly Detection Terran Lane, Carla E. Brodley

An Introduction to MCMC for Machine Learning Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan

Analysis and Visualization of Gene Expression Microarray Data in Human Cancer Using Self-Organizing Maps Sampsa Hautaniemi, Olli Yli-Harja, Jaakko Astola, Päivikki Kauraniemi, Anne Kallioniemi, Maija Wolf, Jimmy Ruiz, Spyro Mousses, Olli-P. Kallioniemi

Being Bayesian About Network Structure. a Bayesian Approach to Structure Discovery in Bayesian Networks Nir Friedman, Daphne Koller

Boosting and Hard-Core Set Construction Adam R. Klivans, Rocco A. Servedio

Boosting and Microarray Data Philip M. Long, Vinsensius Berlian Vega Sn

Classification with Bayesian MARS Christopher C. Holmes, Dave Denison

Clustered Partial Linear Regression Luís Torgo, Joaquim Pinto da Costa

Combining Classifiers with Meta Decision Trees Ljupco Todorovski, Saso Dzeroski

Complete Mining of Frequent Patterns from Graphs: Mining Graph Data Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda

Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data Stefano Monti, Pablo Tamayo, Jill P. Mesirov, Todd R. Golub

EM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence Frank Dellaert, Steven M. Seitz, Charles E. Thorpe, Sebastian Thrun

Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces Jürgen Forster, Niels Schmitt, Hans Ulrich Simon, Thorsten Suttorp

External Control in Markovian Genetic Regulatory Networks Aniruddha Datta, Ashish Choudhary, Michael L. Bittner, Edward R. Dougherty

Feature Weighting in K-Means Clustering Dharmendra S. Modha, W. Scott Spangler

Implications of the Dirichlet Assumption for Discretization of Continuous Variables in Naive Bayesian Classifiers Chun-Nan Hsu, Hung-Ju Huang, Tzu-Tsung Wong

Improved Rooftop Detection in Aerial Images with Machine Learning Marcus A. Maloof, Pat Langley, Thomas O. Binford, Ramakant Nevatia, Stephanie Sage

Improving Markov Chain Monte Carlo Model Search for Data Mining Paolo Giudici, Robert Castelo

Inclusion of Textual Documentation in the Analysis of Multidimensional Data Sets: Application to Gene Expression Data Soumya Raychaudhuri, Hinrich Schütze, Russ B. Altman

Inference for the Generalization Error Claude Nadeau, Yoshua Bengio

Learning from Cluster Examples Toshihiro Kamishima, Fumio Motoyoshi

Learning from Different Teachers Dana Angluin, Martins Krikis

Learning to Match the Schemas of Data Sources: A Multistrategy Approach AnHai Doan, Pedro M. Domingos, Alon Y. Halevy

Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy Ludmila I. Kuncheva, Christopher J. Whitaker

Microchoice Bounds and Self Bounding Learning Algorithms John Langford, Avrim Blum

Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation Ernest Fokoué, D. M. Titterington

On Learning Gene Regulatory Networks Under the Boolean Network Model Harri Lähdesmäki, Ilya Shmulevich, Olli Yli-Harja

Online Ensemble Learning: An Empirical Study Alan Fern, Robert Givan

PAC-Bayesian Stochastic Model Selection David A. McAllester

Polynomial-Time Decomposition Algorithms for Support Vector Machines Don R. Hush, Clint Scovel

Population Markov Chain Monte Carlo Kathryn B. Laskey, James W. Myers

Potential-Based Algorithms in On-Line Prediction and Game Theory Nicolò Cesa-Bianchi, Gábor Lugosi

Programming by Demonstration Using Version Space Algebra Tessa A. Lau, Steven A. Wolfman, Pedro M. Domingos, Daniel S. Weld

Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results Pavel Brazdil, Carlos Soares, Joaquim Pinto da Costa

Relation Between Permutation-Test P Values and Classifier Error Estimates Tailen Hsing, Sanju Attoor, Edward R. Dougherty

Relative Loss Bounds for Temporal-Difference Learning Jürgen Forster, Manfred K. Warmuth

Self-Organizing Latent Lattice Models for Temporal Gene Expression Profiling Byoung-Tak Zhang, Jinsan Yang, Sung Wook Chi

The Robustness of the P-Norm Algorithms Claudio Gentile

Theoretical and Empirical Analysis of ReliefF and RReliefF Marko Robnik-Sikonja, Igor Kononenko

Tree Induction for Probability-Based Ranking Foster J. Provost, Pedro M. Domingos

Variance and Bias for General Loss Functions Gareth James