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