22. ICML 2005:
Bonn,
Germany
Luc De Raedt, Stefan Wrobel (Eds.):
Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005), Bonn, Germany, August 7-11, 2005.
ACM International Conference Proceeding Series 119 ACM 2005, ISBN 1-59593-180-5 BibTeX
- Pieter Abbeel, Andrew Y. Ng:
Exploration and apprenticeship learning in reinforcement learning.
1-8
Electronic Edition (ACM DL) BibTeX
- Brigham Anderson, Andrew Moore:
Active learning for Hidden Markov Models: objective functions and algorithms.
9-16
Electronic Edition (ACM DL) BibTeX
- Nicos Angelopoulos, James Cussens:
Tempering for Bayesian C&RT.
17-24
Electronic Edition (ACM DL) BibTeX
- Fabrizio Angiulli:
Fast condensed nearest neighbor rule.
25-32
Electronic Edition (ACM DL) BibTeX
- Francis R. Bach, Michael I. Jordan:
Predictive low-rank decomposition for kernel methods.
33-40
Electronic Edition (ACM DL) BibTeX
- Ron Bekkerman, Ran El-Yaniv, Andrew McCallum:
Multi-way distributional clustering via pairwise interactions.
41-48
Electronic Edition (ACM DL) BibTeX
- Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford, Bianca Zadrozny:
Error limiting reductions between classification tasks.
49-56
Electronic Edition (ACM DL) BibTeX
- Hendrik Blockeel, David Page, Ashwin Srinivasan:
Multi-instance tree learning.
57-64
Electronic Edition (ACM DL) BibTeX
- Michael H. Bowling, Ali Ghodsi, Dana F. Wilkinson:
Action respecting embedding.
65-72
Electronic Edition (ACM DL) BibTeX
- Markus Breitenbach, Gregory Z. Grudic:
Clustering through ranking on manifolds.
73-80
Electronic Edition (ACM DL) BibTeX
- Will Bridewell, Narges Bani Asadi, Pat Langley, Ljupco Todorovski:
Reducing overfitting in process model induction.
81-88
Electronic Edition (ACM DL) BibTeX
- Christopher J. C. Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Gregory N. Hullender:
Learning to rank using gradient descent.
89-96
Electronic Edition (ACM DL) BibTeX
- John Burge, Terran Lane:
Learning class-discriminative dynamic Bayesian networks.
97-104
Electronic Edition (ACM DL) BibTeX
- Sylvain Calinon, Aude Billard:
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM.
105-112
Electronic Edition (ACM DL) BibTeX
- Michael Carney, Padraig Cunningham, Jim Dowling, Ciaran Lee:
Predicting probability distributions for surf height using an ensemble of mixture density networks.
113-120
Electronic Edition (ACM DL) BibTeX
- Yu-Han Chang, Leslie Pack Kaelbling:
Hedged learning: regret-minimization with learning experts.
121-128
Electronic Edition (ACM DL) BibTeX
- Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang:
Variational Bayesian image modelling.
129-136
Electronic Edition (ACM DL) BibTeX
- Wei Chu, Zoubin Ghahramani:
Preference learning with Gaussian processes.
137-144
Electronic Edition (ACM DL) BibTeX
- Wei Chu, S. Sathiya Keerthi:
New approaches to support vector ordinal regression.
145-152
Electronic Edition (ACM DL) BibTeX
- Corinna Cortes, Mehryar Mohri, Jason Weston:
A general regression technique for learning transductions.
153-160
Electronic Edition (ACM DL) BibTeX
- Jacob W. Crandall, Michael A. Goodrich:
Learning to compete, compromise, and cooperate in repeated general-sum games.
161-168
Electronic Edition (ACM DL) BibTeX
- Hal Daumé III, Daniel Marcu:
Learning as search optimization: approximate large margin methods for structured prediction.
169-176
Electronic Edition (ACM DL) BibTeX
- Fernando De la Torre, Takeo Kanade:
Multimodal oriented discriminant analysis.
177-184
Electronic Edition (ACM DL) BibTeX
- Adam Drake, Dan Ventura:
A practical generalization of Fourier-based learning.
185-192
Electronic Edition (ACM DL) BibTeX
- Kurt Driessens, Saso Dzeroski:
Combining model-based and instance-based learning for first order regression.
193-200
Electronic Edition (ACM DL) BibTeX
- Yaakov Engel, Shie Mannor, Ron Meir:
Reinforcement learning with Gaussian processes.
201-208
Electronic Edition (ACM DL) BibTeX
- Roberto Esposito, Lorenza Saitta:
Experimental comparison between bagging and Monte Carlo ensemble classification.
209-216
Electronic Edition (ACM DL) BibTeX
- Thomas Finley, Thorsten Joachims:
Supervised clustering with support vector machines.
217-224
Electronic Edition (ACM DL) BibTeX
- Holger Fröhlich, Jörg K. Wegner, Florian Sieker, Andreas Zell:
Optimal assignment kernels for attributed molecular graphs.
225-232
Electronic Edition (ACM DL) BibTeX
- Pierre Geurts, Louis Wehenkel:
Closed-form dual perturb and combine for tree-based models.
233-240
Electronic Edition (ACM DL) BibTeX
- Mark Girolami, Simon Rogers:
Hierarchic Bayesian models for kernel learning.
241-248
Electronic Edition (ACM DL) BibTeX
- Karen Glocer, Damian Eads, James Theiler:
Online feature selection for pixel classification.
249-256
Electronic Edition (ACM DL) BibTeX
- Eugene Grois, David C. Wilkins:
Learning strategies for story comprehension: a reinforcement learning approach.
257-264
Electronic Edition (ACM DL) BibTeX
- Carlos Guestrin, Andreas Krause, Ajit Paul Singh:
Near-optimal sensor placements in Gaussian processes.
265-272
Electronic Edition (ACM DL) BibTeX
- Gunjan Gupta, Joydeep Ghosh:
Robust one-class clustering using hybrid global and local search.
273-280
Electronic Edition (ACM DL) BibTeX
- Xiaofei He, Deng Cai, Wanli Min:
Statistical and computational analysis of locality preserving projection.
281-288
Electronic Edition (ACM DL) BibTeX
- Matthias Hein, Jean-Yves Audibert:
Intrinsic dimensionality estimation of submanifolds in Rd.
289-296
Electronic Edition (ACM DL) BibTeX
- Katherine A. Heller, Zoubin Ghahramani:
Bayesian hierarchical clustering.
297-304
Electronic Edition (ACM DL) BibTeX
- Mark Herbster, Massimiliano Pontil, Lisa Wainer:
Online learning over graphs.
305-312
Electronic Edition (ACM DL) BibTeX
- Simon I. Hill, Arnaud Doucet:
Adapting two-class support vector classification methods to many class problems.
313-320
Electronic Edition (ACM DL) BibTeX
- Shen-Shyang Ho:
A martingale framework for concept change detection in time-varying data streams.
321-327
Electronic Edition (ACM DL) BibTeX
- Eugene Ie, Jason Weston, William Stafford Noble, Christina S. Leslie:
Multi-class protein fold recognition using adaptive codes.
329-336
Electronic Edition (ACM DL) BibTeX
- Okhtay Ilghami, Héctor Muñoz-Avila, Dana S. Nau, David W. Aha:
Learning approximate preconditions for methods in hierarchical plans.
337-344
Electronic Edition (ACM DL) BibTeX
- Neil Ireson, Fabio Ciravegna, Mary Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli:
Evaluating machine learning for information extraction.
345-352
Electronic Edition (ACM DL) BibTeX
- Rong Jin, Joyce Y. Chai, Luo Si:
Learn to weight terms in information retrieval using category information.
353-360
Electronic Edition (ACM DL) BibTeX
- Rong Jin, Jian Zhang:
A smoothed boosting algorithm using probabilistic output codes.
361-368
Electronic Edition (ACM DL) BibTeX
- Yushi Jing, Vladimir Pavlovic, James M. Rehg:
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes.
369-376
Electronic Edition (ACM DL) BibTeX
- Thorsten Joachims:
A support vector method for multivariate performance measures.
377-384
Electronic Edition (ACM DL) BibTeX
- Thorsten Joachims, John E. Hopcroft:
Error bounds for correlation clustering.
385-392
Electronic Edition (ACM DL) BibTeX
- Sébastien Jodogne, Justus H. Piater:
Interactive learning of mappings from visual percepts to actions.
393-400
Electronic Edition (ACM DL) BibTeX
- Anders Jonsson, Andrew G. Barto:
A causal approach to hierarchical decomposition of factored MDPs.
401-408
Electronic Edition (ACM DL) BibTeX
- Matti Kääriäinen, John Langford:
A comparison of tight generalization error bounds.
409-416
Electronic Edition (ACM DL) BibTeX
- S. Sathiya Keerthi:
Generalized LARS as an effective feature selection tool for text classification with SVMs.
417-424
Electronic Edition (ACM DL) BibTeX
- Rinat Khoussainov, Andreas Heß, Nicholas Kushmerick:
Ensembles of biased classifiers.
425-432
Electronic Edition (ACM DL) BibTeX
- Mikko Koivisto, Kismat Sood:
Computational aspects of Bayesian partition models.
433-440
Electronic Edition (ACM DL) BibTeX
- Stanley Kok, Pedro Domingos:
Learning the structure of Markov logic networks.
441-448
Electronic Edition (ACM DL) BibTeX
- Jeremy Z. Kolter, Marcus A. Maloof:
Using additive expert ensembles to cope with concept drift.
449-456
Electronic Edition (ACM DL) BibTeX
- Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney:
Semi-supervised graph clustering: a kernel approach.
457-464
Electronic Edition (ACM DL) BibTeX
- Thomas Navin Lal, Michael Schröder, N. Jeremy Hill, Hubert Preißl, Thilo Hinterberger, Jürgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas Hofmann, Niels Birbaumer, Bernhard Schölkopf:
A brain computer interface with online feedback based on magnetoencephalography.
465-472
Electronic Edition (ACM DL) BibTeX
- John Langford, Bianca Zadrozny:
Relating reinforcement learning performance to classification performance.
473-480
Electronic Edition (ACM DL) BibTeX
- François Laviolette, Mario Marchand:
PAC-Bayes risk bounds for sample-compressed Gibbs classifiers.
481-488
Electronic Edition (ACM DL) BibTeX
- Quoc V. Le, Alexander J. Smola, Stéphane Canu:
Heteroscedastic Gaussian process regression.
489-496
Electronic Edition (ACM DL) BibTeX
- Rui Leite, Pavel Brazdil:
Predicting relative performance of classifiers from samples.
497-503
Electronic Edition (ACM DL) BibTeX
- Xuejun Liao, Ya Xue, Lawrence Carin:
Logistic regression with an auxiliary data source.
505-512
Electronic Edition (ACM DL) BibTeX
- Yan Liu, Eric P. Xing, Jaime G. Carbonell:
Predicting protein folds with structural repeats using a chain graph model.
513-520
Electronic Edition (ACM DL) BibTeX
- Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio:
Unsupervised evidence integration.
521-528
Electronic Edition (ACM DL) BibTeX
- Daniel Lowd, Pedro Domingos:
Naive Bayes models for probability estimation.
529-536
Electronic Edition (ACM DL) BibTeX
- Sofus A. Macskassy, Foster J. Provost, Saharon Rosset:
ROC confidence bands: an empirical evaluation.
537-544
Electronic Edition (ACM DL) BibTeX
- Rasmus Elsborg Madsen, David Kauchak, Charles Elkan:
Modeling word burstiness using the Dirichlet distribution.
545-552
Electronic Edition (ACM DL) BibTeX
- Sridhar Mahadevan:
Proto-value functions: developmental reinforcement learning.
553-560
Electronic Edition (ACM DL) BibTeX
- Shie Mannor, Dori Peleg, Reuven Y. Rubinstein:
The cross entropy method for classification.
561-568
Electronic Edition (ACM DL) BibTeX
- H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon:
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees.
569-576
Electronic Edition (ACM DL) BibTeX
- Marina Meila:
Comparing clusterings: an axiomatic view.
577-584
Electronic Edition (ACM DL) BibTeX
- Sauro Menchetti, Fabrizio Costa, Paolo Frasconi:
Weighted decomposition kernels.
585-592
Electronic Edition (ACM DL) BibTeX
- Jeff Michels, Ashutosh Saxena, Andrew Y. Ng:
High speed obstacle avoidance using monocular vision and reinforcement learning.
593-600
Electronic Edition (ACM DL) BibTeX
- Sriraam Natarajan, Prasad Tadepalli:
Dynamic preferences in multi-criteria reinforcement learning.
601-608
Electronic Edition (ACM DL) BibTeX
- Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar:
Learning first-order probabilistic models with combining rules.
609-616
Electronic Edition (ACM DL) BibTeX
- DucDung Nguyen, Tu Bao Ho:
An efficient method for simplifying support vector machines.
617-624
Electronic Edition (ACM DL) BibTeX
- Alexandru Niculescu-Mizil, Rich Caruana:
Predicting good probabilities with supervised learning.
625-632
Electronic Edition (ACM DL) BibTeX
- Santiago Ontañón, Enric Plaza:
Recycling data for multi-agent learning.
633-640
Electronic Edition (ACM DL) BibTeX
- Jean-François Paiement, Douglas Eck, Samy Bengio, David Barber:
A graphical model for chord progressions embedded in a psychoacoustic space.
641-648
Electronic Edition (ACM DL) BibTeX
- Lucas Paletta, Gerald Fritz, Christin Seifert:
Q-learning of sequential attention for visual object recognition from informative local descriptors.
649-656
Electronic Edition (ACM DL) BibTeX
- Franz Pernkopf, Jeff A. Bilmes:
Discriminative versus generative parameter and structure learning of Bayesian network classifiers.
657-664
Electronic Edition (ACM DL) BibTeX
- Tadeusz Pietraszek:
Optimizing abstaining classifiers using ROC analysis.
665-672
Electronic Edition (ACM DL) BibTeX
- Barnabás Póczos, András Lörincz:
Independent subspace analysis using geodesic spanning trees.
673-680
Electronic Edition (ACM DL) BibTeX
- Ganesh Ramakrishnan, Krishna Prasad Chitrapura, Raghu Krishnapuram, Pushpak Bhattacharyya:
A model for handling approximate, noisy or incomplete labeling in text classification.
681-688
Electronic Edition (ACM DL) BibTeX
- Carl Edward Rasmussen, Joaquin Quiñonero Candela:
Healing the relevance vector machine through augmentation.
689-696
Electronic Edition (ACM DL) BibTeX
- Soumya Ray, Mark Craven:
Supervised versus multiple instance learning: an empirical comparison.
697-704
Electronic Edition (ACM DL) BibTeX
- Soumya Ray, David Page:
Generalized skewing for functions with continuous and nominal attributes.
705-712
Electronic Edition (ACM DL) BibTeX
- Jason D. M. Rennie, Nathan Srebro:
Fast maximum margin matrix factorization for collaborative prediction.
713-719
Electronic Edition (ACM DL) BibTeX
- Khashayar Rohanimanesh, Sridhar Mahadevan:
Coarticulation: an approach for generating concurrent plans in Markov decision processes.
720-727
Electronic Edition (ACM DL) BibTeX
- Bernard Rosell, Lisa Hellerstein, Soumya Ray, David Page:
Why skewing works: learning difficult Boolean functions with greedy tree learners.
728-735
Electronic Edition (ACM DL) BibTeX
- Dan Roth, Wen-tau Yih:
Integer linear programming inference for conditional random fields.
736-743
Electronic Edition (ACM DL) BibTeX
- Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor:
Learning hierarchical multi-category text classification models.
744-751
Electronic Edition (ACM DL) BibTeX
- Jarkko Salojärvi, Kai Puolamäki, Samuel Kaski:
Expectation maximization algorithms for conditional likelihoods.
752-759
Electronic Edition (ACM DL) BibTeX
- Sajama, Alon Orlitsky:
Estimating and computing density based distance metrics.
760-767
Electronic Edition (ACM DL) BibTeX
- Sajama, Alon Orlitsky:
Supervised dimensionality reduction using mixture models.
768-775
Electronic Edition (ACM DL) BibTeX
- Bernhard Schölkopf, Florian Steinke, Volker Blanz:
Object correspondence as a machine learning problem.
776-783
Electronic Edition (ACM DL) BibTeX
- Fei Sha, Lawrence K. Saul:
Analysis and extension of spectral methods for nonlinear dimensionality reduction.
784-791
Electronic Edition (ACM DL) BibTeX
- Amnon Shashua, Tamir Hazan:
Non-negative tensor factorization with applications to statistics and computer vision.
792-799
Electronic Edition (ACM DL) BibTeX
- Sajid M. Siddiqi, Andrew W. Moore:
Fast inference and learning in large-state-space HMMs.
800-807
Electronic Edition (ACM DL) BibTeX
- Ricardo Silva, Richard Scheines:
New d-separation identification results for learning continuous latent variable models.
808-815
Electronic Edition (ACM DL) BibTeX
- Özgür Simsek, Alicia P. Wolfe, Andrew G. Barto:
Identifying useful subgoals in reinforcement learning by local graph partitioning.
816-823
Electronic Edition (ACM DL) BibTeX
- Vikas Sindhwani, Partha Niyogi, Mikhail Belkin:
Beyond the point cloud: from transductive to semi-supervised learning.
824-831
Electronic Edition (ACM DL) BibTeX
- Rohit Singh, Nathan Palmer, David K. Gifford, Bonnie Berger, Ziv Bar-Joseph:
Active learning for sampling in time-series experiments with application to gene expression analysis.
832-839
Electronic Edition (ACM DL) BibTeX
- Edward Snelson, Zoubin Ghahramani:
Compact approximations to Bayesian predictive distributions.
840-847
Electronic Edition (ACM DL) BibTeX
- Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf:
Large scale genomic sequence SVM classifiers.
848-855
Electronic Edition (ACM DL) BibTeX
- Alexander L. Strehl, Michael L. Littman:
A theoretical analysis of Model-Based Interval Estimation.
856-863
Electronic Edition (ACM DL) BibTeX
- Qiang Sun, Gerald DeJong:
Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning.
864-871
Electronic Edition (ACM DL) BibTeX
- Yijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu:
Unifying the error-correcting and output-code AdaBoost within the margin framework.
872-879
Electronic Edition (ACM DL) BibTeX
- Csaba Szepesvári, Rémi Munos:
Finite time bounds for sampling based fitted value iteration.
880-887
Electronic Edition (ACM DL) BibTeX
- Brian Tanner, Richard S. Sutton:
TD(lambda) networks: temporal-difference networks with eligibility traces.
888-895
Electronic Edition (ACM DL) BibTeX
- Benjamin Taskar, Vassil Chatalbashev, Daphne Koller, Carlos Guestrin:
Learning structured prediction models: a large margin approach.
896-903
Electronic Edition (ACM DL) BibTeX
- Marc Toussaint, Sethu Vijayakumar:
Learning discontinuities with products-of-sigmoids for switching between local models.
904-911
Electronic Edition (ACM DL) BibTeX
- Ivor W. Tsang, James T. Kwok, Kimo T. Lai:
Core Vector Regression for very large regression problems.
912-919
Electronic Edition (ACM DL) BibTeX
- Koji Tsuda:
Propagating distributions on a hypergraph by dual information regularization.
920-927
Electronic Edition (ACM DL) BibTeX
- Sriharsha Veeramachaneni, Diego Sona, Paolo Avesani:
Hierarchical Dirichlet model for document classification.
928-935
Electronic Edition (ACM DL) BibTeX
- Christian Walder, Olivier Chapelle, Bernhard Schölkopf:
Implicit surface modelling as an eigenvalue problem.
936-939
Electronic Edition (ACM DL) BibTeX
- Chang Wang, Stephen D. Scott:
New kernels for protein structural motif discovery and function classification.
940-947
Electronic Edition (ACM DL) BibTeX
- Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng:
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields.
948-955
Electronic Edition (ACM DL) BibTeX
- Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans:
Bayesian sparse sampling for on-line reward optimization.
956-963
Electronic Edition (ACM DL) BibTeX
- Eric Wiewiora:
Learning predictive representations from a history.
964-971
Electronic Edition (ACM DL) BibTeX
- David Williams, Xuejun Liao, Ya Xue, Lawrence Carin:
Incomplete-data classification using logistic regression.
972-979
Electronic Edition (ACM DL) BibTeX
- Britton Wolfe, Michael R. James, Satinder P. Singh:
Learning predictive state representations in dynamical systems without reset.
980-987
Electronic Edition (ACM DL) BibTeX
- Jianxin Wu, Matthew D. Mullin, James M. Rehg:
Linear Asymmetric Classifier for cascade detectors.
988-995
Electronic Edition (ACM DL) BibTeX
- Mingrui Wu, Bernhard Schölkopf, Gökhan H. Bakir:
Building Sparse Large Margin Classifiers.
996-1003
Electronic Edition (ACM DL) BibTeX
- Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel:
Dirichlet enhanced relational learning.
1004-1011
Electronic Edition (ACM DL) BibTeX
- Kai Yu, Volker Tresp, Anton Schwaighofer:
Learning Gaussian processes from multiple tasks.
1012-1019
Electronic Edition (ACM DL) BibTeX
- Harry Zhang, Liangxiao Jiang, Jiang Su:
Augmenting naive Bayes for ranking.
1020-1027
Electronic Edition (ACM DL) BibTeX
- Ding Zhou, Jia Li, Hongyuan Zha:
A new Mallows distance based metric for comparing clusterings.
1028-1035
Electronic Edition (ACM DL) BibTeX
- Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf:
Learning from labeled and unlabeled data on a directed graph.
1036-1043
Electronic Edition (ACM DL) BibTeX
- Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Ying Ma:
2D Conditional Random Fields for Web information extraction.
1044-1051
Electronic Edition (ACM DL) BibTeX
- Xiaojin Zhu, John D. Lafferty:
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning.
1052-1059
Electronic Edition (ACM DL) BibTeX
- Alexander Zien, Joaquin Quiñonero Candela:
Large margin non-linear embedding.
1060-1067
Electronic Edition (ACM DL) BibTeX
Copyright © Tue May 27 17:58:20 2008
by Michael Ley (ley@uni-trier.de)
