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Title:
Author:
Dale Schuurmans: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Adam J. Grove, Dale Schuurmans
    Boosting in the Limit: Maximizing the Margin of Learned Ensembles. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1998, pp:692-699 [Conf]
  2. Relu Patrascu, Pascal Poupart, Dale Schuurmans, Craig Boutilier, Carlos Guestrin
    Greedy Linear Value-Approximation for Factored Markov Decision Processes. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 2002, pp:285-291 [Conf]
  3. Pascal Poupart, Craig Boutilier, Relu Patrascu, Dale Schuurmans
    Piecewise Linear Value Function Approximation for Factored MDPs. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 2002, pp:292-299 [Conf]
  4. Dale Schuurmans
    A New Metric-Based Approach to Model Selection. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1997, pp:552-558 [Conf]
  5. Dale Schuurmans, Lloyd Greenwald
    Efficient exploration for optimizing immediate reward. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1999, pp:385-392 [Conf]
  6. Dale Schuurmans, Finnegan Southey
    Local Search Characteristics of Incomplete SAT Procedures. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 2000, pp:297-302 [Conf]
  7. Tao Wang, Pascal Poupart, Michael Bowling, Dale Schuurmans
    Compact, Convex Upper Bound Iteration for Approximate POMDP Planning. [Citation Graph (0, 0)][DBLP]
    AAAI, 2006, pp:- [Conf]
  8. Linli Xu, Koby Crammer, Dale Schuurmans
    Robust Support Vector Machine Training via Convex Outlier Ablation. [Citation Graph (0, 0)][DBLP]
    AAAI, 2006, pp:- [Conf]
  9. Linli Xu, Dale Schuurmans
    Unsupervised and Semi-Supervised Multi-Class Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    AAAI, 2005, pp:904-910 [Conf]
  10. Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, Dale Schuurmans
    Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling. [Citation Graph (0, 0)][DBLP]
    ACL, 2006, pp:- [Conf]
  11. Fletcher Lu, Dale Schuurmans
    Model-Based Least-Squares Policy Evaluation. [Citation Graph (0, 0)][DBLP]
    Canadian Conference on AI, 2003, pp:342-352 [Conf]
  12. Xiangji Huang, Fuchun Peng, Aijun An, Dale Schuurmans, Nick Cercone
    Session Boundary Detection for Association Rule Learning Using n-Gram Language Models. [Citation Graph (0, 0)][DBLP]
    Canadian Conference on AI, 2003, pp:237-251 [Conf]
  13. Shaojun Wang, Dale Schuurmans
    Learning Continuous Latent Variable Models with Bregman Divergences. [Citation Graph (0, 0)][DBLP]
    ALT, 2003, pp:190-204 [Conf]
  14. Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone
    Investigating the Relationship between Word Segmentation Performance and Retrieval Performance in Chinese IR. [Citation Graph (0, 0)][DBLP]
    COLING, 2002, pp:- [Conf]
  15. Adam J. Grove, Nick Littlestone, Dale Schuurmans
    General Convergence Results for Linear Discriminant Updates. [Citation Graph (0, 0)][DBLP]
    COLT, 1997, pp:171-183 [Conf]
  16. Dale Schuurmans, Russell Greiner
    Sequential PAC Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1995, pp:377-384 [Conf]
  17. Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans
    Constraint-Based Optimization with the Minimax Decision Criterion. [Citation Graph (0, 0)][DBLP]
    CP, 2003, pp:168-182 [Conf]
  18. Ali Ghodsi, Jiayuan Huang, Finnegan Southey, Dale Schuurmans
    Tangent-Corrected Embedding. [Citation Graph (0, 0)][DBLP]
    CVPR (1), 2005, pp:518-525 [Conf]
  19. Feng Jiao, Stan Z. Li, Heung-Yeung Shum, Dale Schuurmans
    Face Alignment Using Statistical Models and Wavelet Features. [Citation Graph (0, 0)][DBLP]
    CVPR (1), 2003, pp:321-327 [Conf]
  20. Fuchun Peng, Dale Schuurmans, Vlado Keselj, Shaojun Wang
    Language Independent Authorship Attribution with Character Level N-Grams. [Citation Graph (0, 0)][DBLP]
    EACL, 2003, pp:267-274 [Conf]
  21. Russell Greiner, Dale Schuurmans
    Learning an Optimally Accurate Representation System. [Citation Graph (0, 0)][DBLP]
    ECAI Workshop on Knowledge Representation and Reasoning, 1992, pp:145-159 [Conf]
  22. Ali Ghodsi, Jiayuan Huang, Dale Schuurmans
    Transformation-Invariant Embedding for Image Analysis. [Citation Graph (0, 0)][DBLP]
    ECCV (4), 2004, pp:519-530 [Conf]
  23. Fuchun Peng, Dale Schuurmans
    Combining Naive Bayes and n-Gram Language Models for Text Classification. [Citation Graph (0, 0)][DBLP]
    ECIR, 2003, pp:335-350 [Conf]
  24. Dale Schuurmans
    Characterizing rational versus exponential learning curves. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1995, pp:272-286 [Conf]
  25. Dale Schuurmans, Jonathan Schaeffer
    Representational Difficulties with Classifier Systems. [Citation Graph (0, 0)][DBLP]
    ICGA, 1989, pp:328-333 [Conf]
  26. Shaojun Wang, Shaomin Wang, Li Cheng, Russell Greiner, Dale Schuurmans
    Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model. [Citation Graph (0, 0)][DBLP]
    ICGI, 2006, pp:97-111 [Conf]
  27. Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang
    Variational Bayesian image modelling. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:129-136 [Conf]
  28. Carlos Guestrin, Relu Patrascu, Dale Schuurmans
    Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:235-242 [Conf]
  29. Fletcher Lu, Relu Patrascu, Dale Schuurmans
    Investigating the Maximum Likelihood Alternative to TD(lambda). [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:403-410 [Conf]
  30. Dale Schuurmans, Finnegan Southey
    An Adaptive Regularization Criterion for Supervised Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:847-854 [Conf]
  31. Dale Schuurmans, Lyle H. Ungar, Dean P. Foster
    Characterizing the generalization performance of model selection strategies. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:340-348 [Conf]
  32. Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans
    Bayesian sparse sampling for on-line reward optimization. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:956-963 [Conf]
  33. Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
    Learning Mixture Models with the Latent Maximum Entropy Principle. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:784-791 [Conf]
  34. Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng
    Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:948-955 [Conf]
  35. Linli Xu, Dana F. Wilkinson, Finnegan Southey, Dale Schuurmans
    Discriminative unsupervised learning of structured predictors. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:1057-1064 [Conf]
  36. Fuchun Peng, Dale Schuurmans
    Self-Supervised Chinese Word Segmentation. [Citation Graph (0, 0)][DBLP]
    IDA, 2001, pp:238-247 [Conf]
  37. Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans
    Regret-based Utility Elicitation in Constraint-based Decision Problems. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2005, pp:929-934 [Conf]
  38. Yuhong Guo, Russell Greiner, Dale Schuurmans
    Learning Coordination Classifiers. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2005, pp:714-721 [Conf]
  39. Dale Schuurmans, Russell Greiner
    Practical PAC Learning. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1995, pp:1169-1177 [Conf]
  40. Dale Schuurmans, Finnegan Southey, Robert C. Holte
    The Exponentiated Subgradient Algorithm for Heuristic Boolean Programming. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2001, pp:334-341 [Conf]
  41. Daniel J. Lizotte, Tao Wang, Michael Bowling, Dale Schuurmans
    Automatic Gait Optimization with Gaussian Process Regression. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:944-949 [Conf]
  42. Qin Iris Wang, Dekang Lin, Dale Schuurmans
    Simple Training of Dependency Parsers via Structured Boosting. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:1756-1762 [Conf]
  43. Russell Greiner, Dale Schuurmans
    Learning Useful Horn Approximations. [Citation Graph (0, 0)][DBLP]
    KR, 1992, pp:383-392 [Conf]
  44. Fuchun Peng, Dale Schuurmans, Shaojun Wang
    Language and Task Independent Text Categorization with Simple Language Models. [Citation Graph (0, 0)][DBLP]
    HLT-NAACL, 2003, pp:- [Conf]
  45. Dale Schuurmans
    Greedy Importance Sampling. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:596-602 [Conf]
  46. Dale Schuurmans, Relu Patrascu
    Direct value-approximation for factored MDPs. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:1579-1586 [Conf]
  47. Finnegan Southey, Dale Schuurmans, Ali Ghodsi
    Regularized Greedy Importance Sampling. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:753-760 [Conf]
  48. Linli Xu, James Neufeld, Bryce Larson, Dale Schuurmans
    Maximum Margin Clustering. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  49. Fuchun Peng, Dale Schuurmans
    A Simple Closed-Class/Open-Class Factorization for Improved Language Modeling. [Citation Graph (0, 0)][DBLP]
    NLPRS, 2001, pp:145-152 [Conf]
  50. Fuchun Peng, Dale Schuurmans
    A Hierarchical EM Approach to Word Segmentation. [Citation Graph (0, 0)][DBLP]
    NLPRS, 2001, pp:475-480 [Conf]
  51. Jiayuan Huang, Tingshao Zhu, Russell Greiner, Dengyong Zhou, Dale Schuurmans
    Information Marginalization on Subgraphs. [Citation Graph (0, 0)][DBLP]
    PKDD, 2006, pp:199-210 [Conf]
  52. Jiayuan Huang, Tingshao Zhu, Dale Schuurmans
    Web Communities Identification from Random Walks. [Citation Graph (0, 0)][DBLP]
    PKDD, 2006, pp:187-198 [Conf]
  53. Fuchun Peng, Xiangji Huang, Dale Schuurmans, Nick Cercone, Stephen E. Robertson
    Using self-supervised word segmentation in Chinese information retrieval. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2002, pp:349-350 [Conf]
  54. Russell Greiner, Adam J. Grove, Dale Schuurmans
    Learning Bayesian Nets that Perform Well. [Citation Graph (0, 0)][DBLP]
    UAI, 1997, pp:198-207 [Conf]
  55. Fletcher Lu, Dale Schuurmans
    Monte Carlo Matrix Inversion Policy Evaluation. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:386-393 [Conf]
  56. Dale Schuurmans, Finnegan Southey
    Monte Carlo inference via greedy importance sampling. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:523-532 [Conf]
  57. Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
    Boltzmann Machine Learning with the Latent Maximum Entropy Principle. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:567-574 [Conf]
  58. Fuchun Peng, Xiangji Huang, Dale Schuurmans, Shaojun Wang
    Text classification in Asian languages without word segmentation. [Citation Graph (0, 0)][DBLP]
    IRAL, 2003, pp:41-48 [Conf]
  59. Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans
    Constraint-based optimization and utility elicitation using the minimax decision criterion. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 2006, v:170, n:8-9, pp:686-713 [Journal]
  60. Dale Schuurmans, Finnegan Southey
    Local search characteristics of incomplete SAT procedures. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 2001, v:132, n:2, pp:121-150 [Journal]
  61. Fuchun Peng, Dale Schuurmans, Shaojun Wang
    Augmenting Naive Bayes Classifiers with Statistical Language Models. [Citation Graph (0, 0)][DBLP]
    Inf. Retr., 2004, v:7, n:3-4, pp:317-345 [Journal]
  62. Xiangji Huang, Fuchun Peng, Dale Schuurmans, Nick Cercone, Stephen E. Robertson
    Applying Machine Learning to Text Segmentation for Information Retrieval. [Citation Graph (0, 0)][DBLP]
    Inf. Retr., 2003, v:6, n:3-4, pp:333-362 [Journal]
  63. Xiangji Huang, Fuchun Peng, Aijun An, Dale Schuurmans
    Dynamic Web log session identification with statistical language models. [Citation Graph (0, 0)][DBLP]
    JASIST, 2004, v:55, n:14, pp:1290-1303 [Journal]
  64. Dale Schuurmans
    Characterizing Rational Versus Exponential learning Curves. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1997, v:55, n:1, pp:140-160 [Journal]
  65. Yoshua Bengio, Dale Schuurmans
    Guest Introduction: Special Issue on New Methods for Model Selection and Model Combination. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:48, n:1-3, pp:5-7 [Journal]
  66. Adam J. Grove, Nick Littlestone, Dale Schuurmans
    General Convergence Results for Linear Discriminant Updates. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:43, n:3, pp:173-210 [Journal]
  67. Dale Schuurmans, Finnegan Southey
    Metric-Based Methods for Adaptive Model Selection and Regularization. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:48, n:1-3, pp:51-84 [Journal]
  68. Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
    Combining Statistical Language Models via the Latent Maximum Entropy Principle. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2005, v:60, n:1-3, pp:229-250 [Journal]
  69. Ali Ghodsi, Dale Schuurmans
    Automatic basis selection techniques for RBF networks. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2003, v:16, n:5-6, pp:809-816 [Journal]
  70. Tibério S. Caetano, Terry Caelli, Dale Schuurmans, Dante Augusto Couto Barone
    Graphical Models and Point Pattern Matching. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Pattern Anal. Mach. Intell., 2006, v:28, n:10, pp:1646-1663 [Journal]
  71. Chi-Hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner
    Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:793-800 [Conf]
  72. Li Cheng, S. V. N. Vishwanathan, Dale Schuurmans, Shaojun Wang, Terry Caelli
    implicit Online Learning with Kernels. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:249-256 [Conf]
  73. Yuhong Guo, Dale Schuurmans
    Learning Gene Regulatory Networks via Globally Regularized Risk Minimization. [Citation Graph (0, 0)][DBLP]
    RECOMB-CG, 2007, pp:83-95 [Conf]
  74. Yuhong Guo, Dana F. Wilkinson, Dale Schuurmans
    Maximum Margin Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    UAI, 2005, pp:233-242 [Conf]
  75. Yuhong Guo, Dale Schuurmans
    Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering. [Citation Graph (0, 0)][DBLP]
    UAI, 2006, pp:- [Conf]

  76. Semi-Supervised Convex Training for Dependency Parsing. [Citation Graph (, )][DBLP]


  77. An Online Discriminative Approach to Background Subtraction. [Citation Graph (, )][DBLP]


  78. Fast normalized cut with linear constraints. [Citation Graph (, )][DBLP]


  79. Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning. [Citation Graph (, )][DBLP]


  80. Convex Relaxations of Latent Variable Training. [Citation Graph (, )][DBLP]


  81. Stable Dual Dynamic Programming. [Citation Graph (, )][DBLP]


  82. Discriminative Batch Mode Active Learning. [Citation Graph (, )][DBLP]


  83. Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting. [Citation Graph (, )][DBLP]


  84. Policy Iteration for Learning an Exercise Policy for American Options. [Citation Graph (, )][DBLP]


  85. A Reformulation of Support Vector Machines for General Confidence Functions. [Citation Graph (, )][DBLP]


  86. Discriminative Maximum Margin Image Object Categorization with Exact Inference. [Citation Graph (, )][DBLP]


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