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 2005 ACM International Conference Proceeding Series 119 ISBN 1-59593-180-5- Brigham Anderson, Andrew Moore: Active learning for Hidden Markov Models: objective functions and algorithms. 9-16
- Fabrizio Angiulli: Fast condensed nearest neighbor rule. 25-32
- Ron Bekkerman, Ran El-Yaniv, Andrew McCallum: Multi-way distributional clustering via pairwise interactions. 41-48
- Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny: Error limiting reductions between classification tasks. 49-56
- 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
- Sylvain Calinon, Aude Billard: Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM. 105-112
- Michael Carney, Padraig Cunningham, Jim Dowling, Ciaran Lee: 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
- 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
- Kurt Driessens, Saso Dzeroski: Combining model-based and instance-based learning for first order regression. 193-200
- Roberto Esposito, Lorenza Saitta: Experimental comparison between bagging and Monte Carlo ensemble classification. 209-216
- Holger Fröhlich, Jörg K. Wegner, Florian Sieker, Andreas Zell: Optimal assignment kernels for attributed molecular graphs. 225-232
- 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
- 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, Mary Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli: 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
- Yushi Jing, Vladimir Pavlovic, James M. Rehg: 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
- Sébastien Jodogne, Justus H. Piater: 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
- S. Sathiya Keerthi: Generalized LARS as an effective feature selection tool for text classification with SVMs. 417-424
- 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
- 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
- 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
- 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
- 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, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar: Learning first-order probabilistic models with combining rules. 609-616
- Alexandru Niculescu-Mizil, Rich Caruana: Predicting good probabilities with supervised learning. 625-632
- Jean-François Paiement, Douglas Eck, Samy Bengio, David Barber: A graphical model for chord progressions embedded in a psychoacoustic space. 641-648
- Lucas Paletta, Gerald Fritz, Christin Seifert: Q-learning of sequential attention for visual object recognition from informative local descriptors. 649-656
- Franz Pernkopf, Jeff A. Bilmes: 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, Mark Craven: 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
- 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
- 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
- Ricardo Silva, Richard Scheines: New d-separation identification results for learning continuous latent variable models. 808-815
- Özgür Simsek, Alicia P. Wolfe, Andrew G. Barto: 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 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 Snelson, Zoubin Ghahramani: Compact approximations to Bayesian predictive distributions. 840-847
- Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf: 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
- 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, James T. Kwok, Kimo T. Lai: 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, Michael H. Bowling, Dale Schuurmans: 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 P. 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
- 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
- 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
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last updated on 2013-10-16T203152 by the dblp team