21. ICML 2004: Banff, Alberta, Canada
Carla E. Brodley (Ed.): Machine Learning, Proceedings of the Twenty-first International Conference (ICML 2004), Banff, Alberta, Canada, July 4-8, 2004. ACM 2004 ACM International Conference Proceeding Series 69
Klaus Brinker: Active learning of label ranking functions.
Tong Zhang: Solving large scale linear prediction problems using stochastic gradient descent algorithms.
Lourdes Peña Castillo, Stefan Wrobel: A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning.
Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier: Boosting grammatical inference with confidence oracles.
John D. Lafferty, Xiaojin Zhu, Yan Liu: Kernel conditional random fields: representation and clique selection.
Remco R. Bouckaert: Estimating replicability of classifier learning experiments.
Daniel Grossman, Pedro Domingos: Learning Bayesian network classifiers by maximizing conditional likelihood.
Daniil Ryabko: Online learning of conditionally I.I.D. data.
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun: Support vector machine learning for interdependent and structured output spaces.
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung: Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model.

Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul: Learning a kernel matrix for nonlinear dimensionality reduction.
Jieping Ye: Generalized low rank approximations of matrices.
Jieping Ye, Ravi Janardan, Qi Li, Haesun Park: Feature extraction via generalized uncorrelated linear discriminant analysis.


Ran Gilad-Bachrach, Amir Navot, Naftali Tishby: Margin based feature selection - theory and algorithms.
Özgür Simsek, Andrew G. Barto: Using relative novelty to identify useful temporal abstractions in reinforcement learning.
Wei Chu, Zoubin Ghahramani, David L. Wild: A graphical model for protein secondary structure prediction.
Shie Mannor, Ishai Menache, Amit Hoze, Uri Klein: Dynamic abstraction in reinforcement learning via clustering.
George Forman: A pitfall and solution in multi-class feature selection for text classification.
Odest Chadwicke Jenkins, Maja J. Mataric: A spatio-temporal extension to Isomap nonlinear dimension reduction.
Michael R. James, Satinder P. Singh: Learning and discovery of predictive state representations in dynamical systems with reset.
Mikhail Bilenko, Sugato Basu, Raymond J. Mooney: Integrating constraints and metric learning in semi-supervised clustering.
Sheng Gao, Wen Wu, Chin-Hui Lee, Tat-Seng Chua: A MFoM learning approach to robust multiclass multi-label text categorization.
Jianguo Lee, Jingdong Wang, Changshui Zhang, Zhaoqi Bian: Probabilistic tangent subspace: a unified view.

Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu: Learning large margin classifiers locally and globally.
Robert B. Gramacy, Herbert K. H. Lee, William G. Macready: Parameter space exploration with Gaussian process trees.
Zhihua Zhang, Dit-Yan Yeung, James T. Kwok: Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm.

Cristian Sminchisescu, Allan D. Jepson: Generative modeling for continuous non-linearly embedded visual inference.
Duncan Potts: Incremental learning of linear model trees.




Antonio Bahamonde, Gustavo F. Bayón, Jorge Díez, José Ramón Quevedo, Oscar Luaces, Juan José del Coz, Jaime Alonso, Félix Goyache: Feature subset selection for learning preferences: a case study.

Malcolm J. A. Strens: Efficient hierarchical MCMC for policy search.
Hisashi Kashima, Yuta Tsuboi: Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs.
Eduardo F. Morales, Claude Sammut: Learning to fly by combining reinforcement learning with behavioural cloning.


Evgeniy Gabrilovich, Shaul Markovitch: Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5.
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall: Boosting margin based distance functions for clustering.
Artur Merke, Ralf Schoknecht: Convergence of synchronous reinforcement learning with linear function approximation.

Ting Su, Jennifer G. Dy: Automated hierarchical mixtures of probabilistic principal component analyzers.

Max Welling, Michal Rosen-Zvi, Yee Whye Teh: Approximate inference by Markov chains on union spaces.
Annalisa Appice, Michelangelo Ceci, Simon Rawles, Peter A. Flach: Redundant feature elimination for multi-class problems.

Saharon Rosset: Model selection via the AUC.
Shie Mannor, Duncan Simester, Peng Sun, John N. Tsitsiklis: Bias and variance in value function estimation.
Rómer Rosales, Kannan Achan, Brendan J. Frey: Learning to cluster using local neighborhood structure.
Qingping Tao, Stephen D. Scott, N. V. Vinodchandran, Thomas Takeo Osugi: SVM-based generalized multiple-instance learning via approximate box counting.


Glenn Fung, Murat Dundar, Jinbo Bi, R. Bharat Rao: A fast iterative algorithm for fisher discriminant using heterogeneous kernels.
Matthew R. Rudary, Satinder P. Singh, Martha E. Pollack: Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning.
Steven J. Phillips, Miroslav Dudík, Robert E. Schapire: A maximum entropy approach to species distribution modeling.
Xiaoli Zhang Fern, Carla E. Brodley: Solving cluster ensemble problems by bipartite graph partitioning.
Sander M. Bohte, Markus Breitenbach, Gregory Z. Grudic: Nonparametric classification with polynomial MPMC cascades.
Jihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schölkopf: A kernel view of the dimensionality reduction of manifolds.
Yuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picard, Zoubin Ghahramani: Predictive automatic relevance determination by expectation propagation.
Sharlee Climer, Weixiong Zhang: Take a walk and cluster genes: a TSP-based approach to optimal rearrangement clustering.
Douglas P. Hardin, Ioannis Tsamardinos, Constantin F. Aliferis: A theoretical characterization of linear SVM-based feature selection.
Charles A. Sutton, Khashayar Rohanimanesh, Andrew McCallum: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data.
Eric P. Xing, Roded Sharan, Michael I. Jordan: Bayesian haplo-type inference via the dirichlet process.
Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan: Multiple kernel learning, conic duality, and the SMO algorithm.
Bianca Zadrozny: Learning and evaluating classifiers under sample selection bias.
Tony Jebara: Multi-task feature and kernel selection for SVMs.
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov: Training conditional random fields via gradient tree boosting.
Avrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy: Semi-supervised learning using randomized mincuts.
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu: An information theoretic analysis of maximum likelihood mixture estimation for exponential families.
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes: Ensemble selection from libraries of models.
Yasemin Altun, Thomas Hofmann, Alex J. Smola: Gaussian process classification for segmenting and annotating sequences.

Benjamin M. Marlin, Richard S. Zemel: The multiple multiplicative factor model for collaborative filtering.
XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Decentralized detection and classification using kernel methods.

Matthew Rosencrantz, Geoffrey J. Gordon, Sebastian Thrun: Learning low dimensional predictive representations.
Kristina Toutanova, Christopher D. Manning, Andrew Y. Ng: Learning random walk models for inducing word dependency distributions.


Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, Jean-Philippe Vert: Extensions of marginalized graph kernels.
Cholwich Nattee, Sukree Sinthupinyo, Masayuki Numao, Takashi Okada: Learning first-order rules from data with multiple parts: applications on mining chemical compound data.



