Papers available on-line

• Yu, Y. and Schuurmans, D. (2011) Rank/norm regularization with closed-form solutions: Application to subspace clustering. In Conference on Uncertainty in Artificial Intelligence (UAI-11). uai11.pdf

• Zhang, X., Yu, Y., White, M., Huang, R. and Schuurmans, D. (2011) Convex sparse coding, subspace learning, and semi-supervised extensions. In Annual Conference on Artificial Intelligence (AAAI-11). aaai11a.pdf

• Guo, Y. and Schuurmans, D. (2011) Adaptive large margin training for multilabel classification. In Annual Conference on Artificial Intelligence (AAAI-11). aaai11b.pdf

• Lizotte, D., Greiner, R. and Schuurmans, D. (2011) An Experimental Methodology for Response Surface Optimization Methods. In Journal of Global Optimization. jogo11a.pdf

• Peng, J., Mukhergee, L., Singh, V., Schuurmans, D. and Xu, L. (2011) An efficient algorithm for maximal margin clustering. In Journal of Global Optimization. jogo11b.pdf

• Li, W. and Schuurmans, D. (2011) Modular community detection in networks. In International Joint Conference on Artificial Intelligence (IJCAI-11). ijcai11.pdf

• Cheng, L., Gong, M., Schuurmans, D. and Caelli, T. (2011) Real-time discriminative background subtraction. In IEEE Transactions on Image Processing. ip11.pdf

• Yu, Y., Yang, M., Xu, L., White, M. and Schuurmans, D. (2010) Relaxed clipping: A global training method for robust regression and classification. In Advances in Neural Information Processing Systems (NIPS-2010). nips10.pdf

• Quadrianto, N., Schuurmans, D. and Smola, A. (2010) Distributed flow algorithms for scalable similarity visualization. In ICDM 2010 Workshop on Optimization Based Methods for Emerging Data Mining Problems (OEDM-2010). oedm10.pdf

• Bergsma, S., Lin, D., and Schuurmans, D. (2010) Improved natural language learning via variance-regularization support vector machines. In Conference on Computational Natural Language Learning (CoNLL-2010). conll10.pdf

• Shi, Y., Guo, Y., Lin, G., and Schuurmans, D. (2010) Kernel-based gene regulartory network inference. In Conference on Computational Systems Bioinformatics (CSB-2010). csb10.pdf

• Li, W., Xu, L. and Schuurmans, D. (2010) Facility locations revisited: An efficient belief propagation approach. In IEEE International Conference on Automation and Logistics (ICAL-2010). ical10.pdf

• Quadrianto, N., Caetano, T., Lim, J. and Schuurmans, D. (2009) Convex relaxation of mixture regression with efficient algorithms. In Advances in Neural Information Processing Systems (NIPS-2009). nips09a.pdf

• Yu, Y., Li, Y., Szepesvari, C. and Schuurmans, D. (2009) A general projection property for distribution families. In Advances in Neural Information Processing Systems (NIPS-2009). nips09b.pdf

• Xu, L., White, M. and Schuurmans, D. (2009) Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning. In International Conference on Machine Learning (ICML-09). icml09.pdf

• Xu, L., Li, W. and Schuurmans, D. (2009) Fast normalized cut with linear constraints. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR-09). cvpr09.pdf

• Guo, Y. and Schuurmans, D. (2009) A reformulation of support vector machines for general confidence functions. In Asian Conference on Machine Learning (ACML-09). acml09.pdf

• Li, Y., Szepesvari, C. and Schuurmans, D. (2009) Learning exercise policies for American options. In International Conference on Artificial Intelligence and Statistics (AISTATS-09). aistats09a.pdf

• Yang, M., Li, Y. and Schuurmans, D. (2009) Dual temporal difference learning. In International Conference on Artificial Intelligence and Statistics (AISTATS-09). aistats09b.pdf

• Li, Y., Cheng, L. and Schuurmans, D. (2009) Inference of the structural credit risk model using MLE. In IEEE Symposium on Computational Intelligence for Financial Engineering (CIFEr-09). cifer09.pdf

• Shi, Y., Cai, Z., Lin, G. and Schuurmans, D. (2009) Linear coherent bi-cluster discovery via line detection and sample majority voting. In International Conference on Combinatorial Optimization and Applications (COCOA-09). cocoa09.pdf

• Wang, T., Lizotte, D., Bowling, M. and Schuurmans, D. (2008) Dual representations for dynamic programming. dualdp.pdf (29 pages)

• Li, Y. and Schuurmans, D. (2008) Policy iteration for learning and exercise policy for American options. In European Workshop on Reinforcement Learning (EWRL-08). ewrl08.pdf

• Wang, Q., Schuurmans, D. and Lin, D. (2008) Semi-supervised convex training for dependency parsing. In Conference of the Association for Computational Linguistics: Human Language Technologies (ACL-08). acl08.pdf

• Guo, Y. and Schuurmans, D. (2007) Convex relaxations of latent variable training. In Advances in Neural Information Processing Systems (NIPS-2007). nips07b.pdf

• Guo, Y. and Schuurmans, D. (2007) Discriminative batch mode active learning. In Advances in Neural Information Processing Systems (NIPS-2007). nips07c.pdf

• Wang, T., Lizotte, D., Bowling, M. and Schuurmans, D. (2007) Stable dual dynamic programming. In Advances in Neural Information Processing Systems (NIPS-2007). nips07a.pdf

• Wang, T., Bowling, M. and Schuurmans, D. (2007) Dual representations for dynamic programming and reinforcement learning. In IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (IEEE ADPRL-07). adprl07.pdf

• Guo, Y. and Schuurmans, D. (2007) Learning gene regulatory networks via globally regularized risk minimization. In RECOMB Satellite Workshop on Comparative Genomics (RECOMB-CG-07). recombcg07.pdf

• Lizotte, D., Wang, T., Bowling, M. and Schuurmans, D. (2007) Automatic gait optimization with Gaussian process regression. In International Joint Conference on Artificial Intelligence (IJCAI-07). ijcai07a.pdf

• Wang, Q., Lin, D. and Schuurmans, D. (2007) Simple training of dependency parsers via structured boosting. In International Joint Conference on Artificial Intelligence (IJCAI-07). ijcai07b.pdf

• Cheng, L., Vishwanathan, S., Schuurmans, D., Wang, S. and Caelli, T. (2006) Implicit online learning with kernels. In Advances in Neural Information Processing Systems (NIPS-2006). nips06a.pdf

• Lee, C., Wang, S., Jiao, F., Schuurmans, D. and Greiner, R. (2006) Learning to model spatial dependency: semi-supervised discriminative random fields. In Advances in Neural Information Processing Systems (NIPS-2006). nips06b.pdf

• Cheng, L., Wang, S., Schuurmans, D., Caelli, T. and Vishwanathan, S. (2006) An online discriminative approach to background subtraction. In IEEE International Conference on Advanced Video and Signal Based Serveillance (AVSS-2006). avss06.pdf

• Huang, J., Zhu, T., Greiner, R., Zhou, D. and Schuurmans, D. (2006) Information marginalization on subgraphs. In Joint European Conference on Machine Learning and European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD-06). pkdd06a.pdf

• Huang, J., Zhu, T. and Schuurmans, D. (2006) Web communities identification from random walks. In Joint European Conference on Machine Learning and European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD-06). pkdd06b.pdf

• Guo, Y. and Schuurmans, D. (2006) Convex structure learning for Bayesian networks: polynomial feature selection and approximate ordering. In Conference on Uncertainty in Artificial Intelligence (UAI-06). uai06.pdf

• Xu, L., Wilkinson, D., Southey, F. and Schuurmans, D. (2006) Discriminative unsupervised learning of structured predictors. In International Conference on Machine Learning (ICML-06). icml06.pdf

• Xu, L., Crammer, K. and Schuurmans, D. (2006) Robust support vector machine training via convex outlier ablation. In National Conference on Artificial Intelligence (AAAI-06). aaai06a.pdf

• Wang, T., Poupart, P., Bowling, M. and Schuurmans, D. (2006) Compact, convex upper bound iteration for approximate POMDP planning. In National Conference on Artificial Intelligence (AAAI-06). aaai06b.pdf

• Jiao, F., Wang, S., Lee, C., Greiner, R. and Schuurmans, D. (2006) Semi-supervised conditional random fields for improved sequence segmentation and labeling. In Joint Conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics (COLING/ACL-06). acl06.pdf

• Jiao, F., Xu, J., Yu, L. and Schuurmans, D. (2006) Protein fold recognition using the gradient boost algorithm. In Computational Systems Bioinformatics Conference (CSB-06). csb06.pdf

• Wang, Q., Cherry, C., Lizotte, D. and Schuurmans, D. (2006) Improved large margin dependency parsing via local constraints and Laplacian regularization. In Conference on Computational Natural Language Learning (CONLL-06). conll06.pdf

• Caetano, T., Caelli, T., Schuurmans, D. and Barone, D. (2006) Graphical models and point pattern matching. In IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (10):1646-1663. pami06.pdf

• Boutilier, C., Patrascu, R., Poupart, P. and Schuurmans, D. (2006) Constraint-based optimization and utility elicitation using the minimax decision criterion. Artificial Intelligence 170 (8-9): 686-713. aij06.pdf

• Schuurmans, D., Southey, F., Wilkinson, D. and Guo, Y. (2006) Metric-based approaches for semi-supervised regression and classification. In O. Chapelle, B. Schoelkopf, and A. Zien, editors, Semi-Supervised Learning, MIT Press. ssl06.pdf

• Wang, S., Wang, S., Cheng, L., Greiner, R. and Schuurmans, D. (2006) Stochastic analysis of a lexical and semantic enhanced structural language model. In International Colloquium on Grammatical Inference (ICGI-2006). icgi06.pdf

• Guo, Y., Wilkinson, D. and Schuurmans, D. (2005) Maximum margin Bayesian networks. In Conference on Uncertainty in Artificial Intelligence (UAI-05). uai05.pdf

• Xu, L. and Schuurmans, D. (2005) Unsupervised and semi-supervised multi-class support vector machines. In National Conference on Artificial Intelligence (AAAI-05). aaai05.pdf

• Wang, T., Lizotte, D., Bowling, M. and Schuurmans, D. (2005) Bayesian sparse sampling for on-line reward optimization. In International Conference on Machine Learning (ICML-05). icml05a.pdf

• Wang, S., Wang, S., Greiner, R., Schuurmans, D. and Cheng, L. (2005) Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. In International Conference on Machine Learning (ICML-05). icml05b.ps.gz

• Cheng, L., Jiao, F., Schuurmans, D. and Wang, S. (2005) Variational Bayesian image modelling. In International Conference on Machine Learning (ICML-05). icml05c.pdf

• Guo, Y., Greiner, R. and Schuurmans, D. (2005) Learning coordination classifiers. In International Joint Conference on Artificial Intelligence (IJCAI-05). ijcai05a.pdf

• Boutilier, C., Patrascu, R., Poupart, P. and Schuurmans, D. (2005) Regret-based utility elicitation in contraint-based decision problems. In International Joint Conference on Artificial Intelligence (IJCAI-05). ijcai05b.ps.gz

• Ghodsi, A., Southey, F., Huang, J. and Schuurmans, D. (2005) Tangent corrected embedding. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR-05). cvpr05.pdf

• Wang, Q., Schuurmans, D. and Lin, D. (2005) Strictly lexical dependency parsing. In International Workshop on Parsing Technologies (IWPT-2005). iwpt05.pdf

• Wang, Q. and Schuurmans, D. (2005) Improved estimation for unsupervsied part-of-speech tagging. In EEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE'2005). nlpke05.pdf

• Wang, S., Schuurmans, D., Peng, F. and Zhao, Y. (2005) Combining statistical language models via the latent maximum entropy principle. In Machine Learning 60(1-3): 229-250. mlj05.ps.gz

• Xu, L., Neufeld, J., Larson, B. and Schuurmans, D. (2004) Maximum margin clustering. In Advances in Neural Information Processing Systems (NIPS-2004). nips04.ps.gz

• Ghodsi, A., Huang, J. and Schuurmans, D. (2004) Transformation-invariant embedding. In European Conference on Computer Vision (ECCV-04). eccv04.ps.gz

• Huang, J., Peng, F., An, A. and Schuurmans, D. (2004) Dynamic web log session identification with statistical language models. In Journal of the American Society for Information Science and Technology 55(14): 1290--1303. jasist04.ps.gz

• Wang, S., Schuurmans, D., Peng, F. and Zhao, Y. (2004) Learning mixture models with the regularized maximum entropy principle. In IEEE Transactions on Neural Networks 15 (4): 903-916. ieeenn04.ps.gz
  Earlier conference version appeared in International Conference on Machine Learning (ICML-03). icml03.ps.gz

• Wang, S., Schuurmans, D. and Zhao, Y. (2003) The latent maximum entropy principle. lme.ps.gz

• Peng, F., Schuurmans, D. and Wang, S. (2003) Augmenting Naive Bayes classifiers with statistical language models. In Information Retrieval 7 (3): 317-345. jir03b.ps.gz

• Huang, X., Peng, F., Schuurmans, D., Cercone, N. and Robertson, S. (2003) Applying machine learning to text segmentation for information retrieval. In Information Retrieval 6 (3): 333-362. jir03a.ps.gz

• Boutilier, C., Patrascu, R., Poupart, P. and Schuurmans, D. (2003) Constraint-based optimization with the minimax decision criterion. In Conference on Principles and Practice of Constraint Programming (CP-03). cp03.ps.gz

• Lu, F. and Schuurmans, D. (2003) Monte Carlo matrix inversion policy evaluation. In Conference on Uncertainty in Artificial Intelligence (UAI-03). uai03a.ps.gz

• Wang, S., Schuurmans, D., Peng, F. and Zhao, Y. (2003) Boltzmann machine learning with the latent maximum entropy principle. In Conference on Uncertainty in Artificial Intelligence (UAI-03). uai03b.ps.gz

• Peng, F., Schuurmans, D. and Wang, S. (2003) Language and task independent text categorization with simple language models. In Human Language Technology Conference (HLT-NAACL-03). naacl03.ps.gz

• Wang, S. and Schuurmans, D. (2003) Learning continuous latent variable models with Bregman divergences. In International Conference on Algorithmic Learning Theory (ALT-03). alt03.ps.gz

• Lu, F. and Schuurmans, D. (2003) Model-based least-squares policy evaluation. In Sixteenth Canadian Conference on Artificial Intelligence (CAI-03). cai03a.ps.gz

• Huang, X., Peng, F., An, A., Schuurmans, D. and Cercone, N. (2003) Session boundary detection for association rule learning using n-gram language models. In Sixteenth Canadian Conference on Artificial Intelligence (CAI-03). cai03b.ps.gz

• Peng, F., Schuurmans, D., Keselj, V. and Wang, S. (2003) Language independent authorship attribution using character level language models. In Tenth Conference of the European Chapter of the Association for Computational Linguistics (EACL-03). eacl03.ps.gz

• Peng, F. and Schuurmans, D. (2003) Combining naive Bayes and n-gram language models for text classification. In Twenty-Fifth European Conference on Information Retrieval Research (ECIR-03). ecir03.ps.gz

• Wang, S., Schuurmans, D., Peng, F. and Zhao, Y. (2003) Semantic n-gram language modeling with the latent maximum entropy principle. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-03). icassp03.ps.gz

• Wang, S., Schuurmans, D. and Peng, F. (2003) Latent maximum entropy approach for semantic n-gram language modeling. In Ninth International Workshop on Artificial Intelligence and Statistics (AISTATS-03). aistats03.ps.gz

• Ghodsi, A. and Schuurmans, D. (2003) Automatic basis selection techniques for RBF networks. Neural Networks 16 (5-6): 809-816. nn03.ps.gz
  Earlier conference version appeared in International Joint Conference on Neural Networks (IJCNN-03). ijcnn03.ps.gz

• Southey, F., Schuurmans, D. and Ghodsi, A. (2002) Regularized greedy importance sampling. In Advances in Neural Information Processing Systems (NIPS-2002). nips02.ps.gz

• Lu, F., Patrascu, R. and Schuurmans, D. (2002) Investigating the maximum likelihood alternative to TD(). In Proceedings of the Nineteenth International Conference on Machine Learning (ICML-02). icml02a.ps.gz

• Guestrin, C., Patrascu, R. and Schuurmans, D. (2002) Algorithm directed exploration for model-based reinforcement learning in factored MDPs. In Proceedings of the Nineteenth International Conference on Machine Learning (ICML-02). icml02b.ps.gz

• Peng, F., Huang, X., Schuurmans, D. and Cercone, N. (2002) Investigating the relationship between word segmentation performance and retrieval performance in Chinese IR. In Proceedings of the Nineteenth International Conference on Computational Linguistics (COLING-02). coling02.ps.gz

• Peng, F., Huang, X., Schuurmans, D., Cercone, N. and Robertson, S. (2002) Using self-supervised word segmentation in Chinese information retrieval. Short version in Proceedings of the Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-02). sigir02.ps.gz

• Elidan, G., Ninio, M., Friedman, N. and Schuurmans, D. (2002) Data perturbation for escaping local maxima in learning. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02). aaai02a.ps.gz

• Patrascu, R., Poupart, P., Schuurmans, D., Boutilier, C. and Guestrin, C. (2002) Greedy linear value-approximation for factored Markov decision processes. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02). aaai02b.ps.gz

• Poupart, P., Boutilier, C., Patrascu, R. and Schuurmans, D. (2002) Piecewise linear value function approximation for factored MDPs. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02). aaai02c.ps.gz

• Wang, S., Rosenfeld, R., Zhao, Y. and Schuurmans, D. (2002) The latent maximum entropy principle. Short version in 2002 IEEE International Symposium on Information Theory (ISIT-02). isit02.ps.gz

• Schuurmans, D. and Southey, F. (2002) Metric-based methods for adaptive model selection and regularization. Machine Learning 48(1-3): 51-84. Special Issue on New Methods for Model Selection and Model Combination. mlj02.pdf

• Greiner, R., Schuurmans, D. and O'Brien, C. (2002) Efficient estimation exploiting independence constraints. bayest.ps.gz

• Schuurmans, D. and Patrascu, R. (2001) Direct value-approximation for factored MDPs. In Advances in Neural Information Processing 14 (NIPS-2001). nips01.ps.gz

• Peng, F. and Schuurmans, D. (2001) A simple closed-class/open-class factorization for language modeling. In 6th Natural Language Processing Pacific Rim Symposium (NLPRS-2001). nlprs01b.ps.gz

• Peng, F. and Schuurmans, D. (2001) A hierarchical EM approach to word segmentation. In 6th Natural Language Processing Pacific Rim Symposium (NLPRS-2001). nlprs01a.ps.gz

• Peng, F. and Schuurmans, D. (2001) Self-supervised Chinese word segmentation. In Proceedings of the Fourth International Symposium on Intelligent Data Analysis (IDA-2001). ida01.ps.gz

• Schuurmans, D., Southey, F. and Holte, R. (2001) The exponentiated subgradient algorithm for heuristic Boolean programming. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01). ijcai01.ps.gz (See related software)

• Schuurmans, D. and Southey, F. (2001) Local search characteristics of incomplete SAT procedures. Artificial Intelligence 132(2), 121-150. aij01.pdf
  Earlier conference paper appeared in Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000). aaai00.ps.gz
  (See related software)

• Grove, A., Littlestone, N. and Schuurmans, D. (2001) General convergence results for linear discriminant updates. Machine Learning 43(1-3), 173-210. mlj01.pdf
  Earlier conference paper appeared in Proceedings of the Tenth Annual Conference on Computational Learning Theory (COLT-97). colt97.ps.gz

• Schuurmans, D. and Southey, F. (2000) Monte Carlo inference via greedy importance sampling. In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2000). uai00.ps.gz

• Schuurmans, D. and Southey, F. (2000) An adaptive regularization criterion for supervised learning. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000). icml00.ps.gz

• Smola, A., Bartlett, P., Schoelkopf, B., and Schuurmans, D. (2000) Introduction to Large Margin Classifiers. In Advances in Large Margin Classifiers. MIT Press, Cambridge, MA. Smola, A., Bartlett, P., Schoelkopf, B., and Schuurmans, D., editors. lmc.ps.gz

• Schuurmans, D. (1999) Greedy importance sampling. In Advances in Neural Information Processing 12 (NIPS-99). nips99.ps.gz

• Schuurmans, D. and Greenwald, L. (1999) Efficient exploration for optimizing immediate reward. In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99). aaai99.ps.gz

• Grove, A. and Schuurmans, D. (1998) Boosting in the limit: maximizing the margin of learned ensembles. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98). aaai98.ps.gz

• Schuurmans, D. (1997) A new metric-based approach to model selection. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97). aaai97.ps.gz

• Schuurmans, D., Ungar, L. and Foster, D. (1997) Characterizing the generalization performance of model selection strategies. In Proceedings of the Fourteenth International Conference on Machine Learning (ICML-97). icml97.ps.gz

• Greiner, R., Grove, A. and Schuurmans, D. (1997) Learning Bayesian nets that perform well. In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97). uai97.ps.gz

• Greiner, R., Grove, A. and Schuurmans, D. (1997) On learning hierarchical classifications. hier.ps.gz

• Schuurmans, D. (1996) Effective Classification Learning. PhD Thesis, Department of Computer Science, University of Toronto. thesis-techrept.pdf (183 pages)

• Schuurmans, D. and Greiner, R. (1995) Practical PAC learning. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95), pp. 1169-1175. ijcai95.ps.gz

• Schuurmans, D. and Greiner, R. (1995) Sequential PAC learning. In Proceedings of the Eighth Annual Conference on Computational Learning Theory (COLT-95), pp. 377-384. colt95.ps.gz

• Schuurmans, D. (1995) Characterizing rational versus exponential learning curves. In Proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT-95) , pp. 272-286. eurocolt95.ps.gz
  Extended paper appears in Journal of Computer and System Sciences 55 (1997), 140-160.

• Schuurmans, D. and Greiner, R. (1994) Learning default concepts. In Proceedings of the Tenth Canadian Conference on Artificial Intelligence (CSCSI-94), pp. 99-106. cscsi94.ps.gz

• Schuurmans, D. and Greiner, R. (1993) Learning to classify incomplete examples. In Computational Learning Theory and Natural Learning Systems, Volume 4. MIT Press, Cambridge, MA. clnl93-incl.ps.gz, (bib)

• Schuurmans, D. (1993) Fast distribution-specific learning. In Computational Learning Theory and Natural Learning Systems, Volume 4. MIT Press, Cambridge, MA. clnl93-seq.ps.gz, (bib)

• Greiner, R. and Schuurmans, D. (1992) Learning useful Horn approximations. In Proceedings of the Third International Conference on Knowledge Representation and Reasoning (KR-92), pp. 383-392. kr92.ps.gz

• Greiner, R. and Schuurmans, D. (1992) Learning an optimally accurate representational system. In Foundations of Knowledge Representation and Reasoning, pp. 145-159. Springer-Verlag, Berlin.

• Cohen, W., Greiner, R., and Schuurmans, D. (1992) Probabilistic hill-climbing. In Computational Learning Theory and Natural Learning systems, Volume 2, pp. 171-181. MIT Press, Cambridge, MA. clnl92.ps.gz