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ORCID21st ICML 2004: Banff, Alberta, Canada
- Carla E. Brodley:
Machine Learning, Proceedings of the Twenty-first International Conference (ICML 2004), Banff, Alberta, Canada, July 4-8, 2004. ACM International Conference Proceeding Series 69, ACM 2004 - Klaus Brinker:
Active learning of label ranking functions. - Tong Zhang:
Solving large scale linear prediction problems using stochastic gradient descent algorithms. - Guy Lebanon, John D. Lafferty:
Hyperplane margin classifiers on the multinomial manifold. - Lourdes Peña Castillo, Stefan Wrobel:
A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning. - Jian Zhang, Yiming Yang:
Probabilistic score estimation with piecewise logistic regression. - 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 M. 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. - Ankur Agarwal, Bill Triggs:
Learning to track 3D human motion from silhouettes. - Nir Krause, Yoram Singer:
Leveraging the margin more carefully. - Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul:
Learning a kernel matrix for nonlinear dimensionality reduction. - Daan Wierstra, Marco A. Wiering:
Utile distinction hidden Markov models. - Jieping Ye:
Generalized low rank approximations of matrices. - Jieping Ye, Ravi Janardan, Qi Li, Haesun Park:
Feature extraction via generalized uncorrelated linear discriminant analysis. - Hieu Tat Nguyen, Arnold W. M. Smeulders:
Active learning using pre-clustering. - Ulf Brefeld, Tobias Scheffer:
Co-EM support vector learning. - Vincent Conitzer, Tuomas Sandholm:
Communication complexity as a lower bound for learning in games. - 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. - Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal:
The Bayesian backfitting relevance vector machine. - Michael R. James, Satinder 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. - Eibe Frank, Stefan Kramer:
Ensembles of nested dichotomies for multi-class problems. - Yongdai Kim, Jinseog Kim:
Gradient LASSO for feature selection. - Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu:
Learning large margin classifiers locally and globally. - Alan Herschtal, Bhavani Raskutti:
Optimising area under the ROC curve using gradient descent. - 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. - Charles X. Ling, Qiang Yang, Jianning Wang, Shichao Zhang:
Decision trees with minimal costs. - Rong Jin, Huan Liu:
Robust feature induction for support vector machines. - Cristian Sminchisescu, Allan D. Jepson:
Generative modeling for continuous non-linearly embedded visual inference. - Duncan Potts:
Incremental learning of linear model trees. - Saher Esmeir, Shaul Markovitch:
Lookahead-based algorithms for anytime induction of decision trees. - Ofer Dekel, Joseph Keshet, Yoram Singer:
Large margin hierarchical classification. - Jaakko Peltonen
, Janne Sinkkonen, Samuel Kaski:
Sequential information bottleneck for finite data. - Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng:
Online and batch learning of pseudo-metrics. - Aleks Jakulin, Ivan Bratko:
Testing the significance of attribute interactions. - 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. - Jong-Hoon Ahn, Seungjin Choi, Jong-Hoon Oh:
A multiplicative up-propagation algorithm. - Ted Scully, Michael G. Madden, Gerard Lyons:
Coalition calculation in a dynamic agent environment. - Malcolm J. A. Strens:
Efficient hierarchical MCMC for policy search. - Neil D. Lawrence, John C. Platt:
Learning to learn with the informative vector machine. - 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. - Prem Melville, Raymond J. Mooney:
Diverse ensembles for active learning. - Roberto Esposito, Lorenza Saitta:
A Monte Carlo analysis of ensemble classification. - Ulrich Rückert, Stefan Kramer:
Towards tight bounds for rule learning. - 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. - Hong Chang, Dit-Yan Yeung:
Locally linear metric adaptation for semi-supervised clustering. - Soumya Ray, David Page:
Sequential skewing: an improved skewing algorithm. - Ting Su, Jennifer G. Dy:
Automated hierarchical mixtures of probabilistic principal component analyzers. - Justin Basilico, Thomas Hofmann:
Unifying collaborative and content-based filtering. - César Ferri, Peter A. Flach, José Hernández-Orallo:
Delegating classifiers. - Max Welling, Michal Rosen-Zvi, Yee Whye Teh:
Approximate inference by Markov chains on union spaces. - Annalisa Appice, Michelangelo Ceci, Simon Alan Rawles, Peter A. Flach:
Redundant feature elimination for multi-class problems. - Nicolas Baskiotis, Michèle Sebag:
C4.5 competence map: a phase transition-inspired approach. - Koby Crammer, Gal Chechik:
A needle in a haystack: local one-class optimization. - Saharon Rosset:
Model selection via the AUC. - Kristian Kersting, Martijn van Otterlo, Luc De Raedt:
Bellman goes relational. - 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. - Tao Li, Sheng Ma, Mitsunori Ogihara:
Entropy-based criterion in categorical clustering. - Qingping Tao, Stephen Donald Scott, N. V. Vinodchandran, Thomas Takeo Osugi:
SVM-based generalized multiple-instance learning via approximate box counting. - Anna Goldenberg, Andrew W. Moore:
Tractable learning of large Bayes net structures from sparse data. - Chris H. Q. Ding, Xiaofeng He:
Linearized cluster assignment via spectral ordering. - Chris H. Q. Ding, Xiaofeng He:
K-means clustering via principal component analysis. - Glenn Fung, Murat Dundar, Jinbo Bi, R. Bharat Rao:
A fast iterative algorithm for fisher discriminant using heterogeneous kernels. - Jelle R. Kok, Nikos Vlassis:
Sparse cooperative Q-learning. - Matthew R. Rudary, Satinder 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. - Austin I. Eliazar, Ronald Parr:
Learning probabilistic motion models for mobile robots. - Xiaoli Zhang Fern, Carla E. Brodley:
Solving cluster ensemble problems by bipartite graph partitioning. - Ronan Collobert, Samy Bengio:
Links between perceptrons, MLPs and SVMs. - Sander M. Bohté, 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. - Alan Fern, Robert Givan:
Relational sequential inference with reliable observations. - Douglas P. Hardin, Ioannis Tsamardinos, Constantin F. Aliferis:
A theoretical characterization of linear SVM-based feature selection. - Charles 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. - Volkan Vural, Jennifer G. Dy:
A hierarchical method for multi-class support vector machines. - 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. - Pieter Abbeel, Andrew Y. Ng:
Apprenticeship learning via inverse reinforcement learning. - 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, Alexander J. Smola:
Gaussian process classification for segmenting and annotating sequences. - Corinna Cortes, Mehryar Mohri:
Distribution kernels based on moments of counts. - Pengcheng Wu, Thomas G. Dietterich:
Improving SVM accuracy by training on auxiliary data sources. - 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. - David M. Blei, Michael I. Jordan:
Variational methods for the Dirichlet process. - David Wingate, Kevin D. Seppi:
P3VI: a partitioned, prioritized, parallel value iterator. - 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. - Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola:
Learning with non-positive kernels. - Benjamin Taskar, Vassil Chatalbashev, Daphne Koller:
Learning associative Markov networks. - Csaba Szepesvári, William D. Smart:
Interpolation-based Q-learning. - 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. - Moshe Koppel, Jonathan Schler:
Authorship verification as a one-class classification problem.
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