C.J.C. Burges, Machine Learning Blog: September 16, 2014, From Stumps to Trees to Forests

C.J.C. Burges, Machine Learning Blog: July 11, 2014, Machine Learning for Industry: A Case Study

R. Gilad-Bachrach and C.J.C. Burges, Classifier Selection using the Predicate Depth. Journal of Machine Learning Research vol. 14, pp. 3591-3618, 2013, 27 pages

J.L. Moore, C.J.C. Burges, E.Renshaw and W. Yih, Animacy Detection with Voting Models. in Empirical Methods in Natural Language Processing (EMNLP), 2013, 6 pages

R. Gilad-Bachrach and C.J.C. Burges, The Median Hypothesis. In Empirical Inference – Festschrift in Honor of Vladimir N. Vapnik ed. by Bernhard Schoelkopf, Zhiyuan Luo, and Vladimir Vovk, Springer, Heidelberg, September 2013, 15 pages

J.C. Platt and C.J.C. Burges, Regularized Least Squared to Remove Reviewer Bias. A note on a method used at several NIPS conferences to detect and remove reviewer bias. May 2012, 4 pages

K. Raman, K.M. Svore, R. Gilad-Bachrach, and C.J.C. Burges, Learning from Our Mistakes: Towards a Correctable Learning Algorithm. In 21st International Conference on Information and Knowledge Management (CIKM), ACM, 27 October 2012, 5 pages

C.J.C. Burges, A Very Brief Look at the Bing Ranking Algorithm. Talk given at the MSR Faculty Summit, July 2011 (PowerPoint with audio), 5 minutes

K.M. Svore and C.J.C. Burges, Large-scale Learning to Rank using Boosted Decision Trees. In Scaling Up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, May 2011, 44 pages

C.J.C. Burges, K.M. Svore, P.N. Bennett, A. Pastusiak and Q. Wu, Learning to Rank Using an Ensemble of Lambda-Gradient Models. Journal of Machine Learning Research: Workshop and Conference Proceedings, vol. 14, pp. 25-35, 2011. This paper describes our winning entry in the Yahoo! Learning to Rank competition. 11 pages

K.M. Svore, M.N. Volkovs and C.J.C. Burges, Learning to Rank with Multiple Objective Functions. Proceedings of the International World Wide Web conference, March, 2011, 10 pages

K.M. Svore and C.J.C. Burges, Learning to Rank on a Cluster using Boosted Decision Trees. In Learning to Rank on Cores, Clusters, and Clouds Workshop, NIPS 2010, 16 pages

S. Jafarpour and C.J.C. Burges, Filter, Rank, and Transfer the Knowledge: Learning to Chat. Microsoft Research Technical Report MSR-TR-2010-93, 2010, 18 pages

C.J.C. Burges, Dimension Reduction: A Guided Tour. Foundations and Trends in Machine Learning, Vol. 2, No. 4, 275-365, 2010, 93 pages

C.J.C. Burges, From RankNet to LambdaRank to LambdaMART: An Overview. Microsoft Research Technical Report MSR-TR-2010-82, 2010, 19 pages (Errata).

Q. Wu, C.J.C. Burges, K. Svore, and J. Gao, Adapting Boosting for Information Retrieval Measures. Journal of Information Retrieval, 2009, 17 pages. This publication is also available at www.springerlink.com.

K. Svore and C.J.C. Burges, A Machine Learning Approach for Improved BM25 Retrieval. CIKM 2009, 4 pages

J. Gao, Q. Wu, C.J.C. Burges, K. Svore, Y. Su, N. Khan, S. Shah and H. Zhou, Model Adaptation via Model Interpolation and Boosting for Web Search Ranking. EMNLP 2009, 9 pages

P. Donmez, K. Svore and C.J.C. Burges, On the Local Optimality of LambdaRank. SIGIR 2009, paper (8 pages), tech report

D. Zhou and C.J.C. Burges, High-Order Regularization in Graphs. Intl. Workshop on Mining and Learning with Graphs, Helsinki, 2008, 8 pages

K.M. Svore, L. Vanderwende, and C.J.C. Burges, Using Signals of Human Interest to Enhance Single-document Summarization. In Proceedings of Association for the Advancement of Artificial Intelligence (AAAI) 2008, Nectar track, 4 pages

Y. Yue and C.J.C. Burges, On Using Simultaneous Perturbation Stochastic Approximation for Learning to Rank, and the Empirical Optimality of LambdaRank. Microsoft Research Technical Report MSR-TR-2007-115, 17 pages

P. Li, C.J.C. Burges and Q. Wu, Learning to Rank Using Classification and Gradient Boosting. NIPS 2007, 10 pages

K. Svore, L. Vanderwende and C.J.C. Burges, Enhancing Single-document Summarization by Combining RankNet and Third-party Sources. EMNLP 2007, 10 pages

D. Zhou and C.J.C. Burges, Spectral Clustering and Transductive Learning with Multiple Views. ICML 2007, 8 pages

K. Svore, Q. Wu, C.J.C. Burges and A. Raman, Improving Web Spam Classification using Rank-time Features. AIRWeb workshop in WWW 2007, 8 pages

D. Zhou, C.J.C. Burges and T. Tao, Transductive Link Spam Detection. AIRWeb workshop in WWW 2007, 8 pages

C.J.C. Burges, R. Ragno and Q.V. Le, Learning to Rank with Non-Smooth Cost Functions. NIPS 2006, 8 pages

I. Matveeva, C.J.C. Burges, T. Burkard, A. Laucius and L. Wong, High Accuracy Retrieval with Multiple Nested Rankers. SIGIR 2006, 8 pages

M. Taylor, H. Zaragoza, H. Craswell, S. Robertson and C.J.C. Burges, Optimisation methods for ranking functions with multiple parameters. CIKM 2006, 9 pages

C.J.C. Burges and J.C. Platt, Semi-Supervised Learning with Conditional Harmonic Mixing. In Semi-Supervised Learning, MIT Press, 2006, Eds. O. Chapelle, B. Schölkopf and A. Zien, 29 pages

C.J.C. Burges, Ranking As Function Approximation. Invited talk, Proc. Algorithms for Approximation 5 (July 2005), Springer 2006, 15 pages

C.J.C. Burges, Ranking as Learning Structured Outputs. In Proceedings of the NIPS 2005 Workshop on Learning to Rank, Editors S. Agarwal, C. Cortes and R. Herbrich, Dec. 2005, 4 pages

R. Ragno, C.J.C. Burges, C. Herley, Inferring Similarity Between Music Objects with Application to Playlist Generation. Proc. 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, Nov. 2005, 8 pages

C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton and G. Hullender, Learning to Rank using Gradient Descent. 22nd International Conference on Machine Learning, Bonn, 2005. Winner of the 2015 ICML Test of Time Best Paper Award. 8 pages

C.J.C. Burges, Geometric Methods for Feature Extraction and Dimensional Reduction. In Data Mining and Knowledge Discovery Handbook: A Complete Guide for Researchers and Practitioners”, Eds. O. Maimon and L. Rokach, Kluwer Academic Publishers, 2005, 36 pages

C.J.C. Burges, Some Notes on Applied Mathematics for Machine Learning. In Proceedings of the 2003 Summer Schools on Machine Learning, Springer Lecture Series on Artificial Intelligence, 2004, 20 pages

C.J.C. Burges and D.J. Crisp, Uniqueness Theorems for Kernel Methods. Neurocomputing, Vol 55, 2003, 36 pages

C.J.C. Burges and D.J. Crisp, Uniqueness of the SVM Solution. NIPS 12, 2000, pp. 223-229, 7 pages

D.J. Crisp and C.J.C. Burges, A Geometric Interpretation of nu-SVM Classifiers. NIPS 12, 2000, pp. 244-250, 7 pages

B. Schölkopf, S. Mika, C.J.C. Burges, P. Knirsch, K.-R. Müller, G. Raetsch and A. Smola, Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 1999, 10:5, pp 1000-1017, 19 pages

C.J.C. Burges, Geometry and Invariance in Kernel Based Methods. In Advances in Kernel Methods – Support Vector Learning, Eds. B. Schölkopf, C.J.C. Burges, A. Smola, MIT Press, Cambridge, USA, 1999, 32 pages

C.R. Nohl, D.S. Lee, J.I. Ben and C.J.C. Burges, A system for address extraction from facsimile images. Proceedings, Symposium on Document Image Understanding Technology, 1999

C.J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, Vol. 2, Number 2, p. 121-167, Kluwer Academic Publishers, 1998, 43 pages

B. Schölkopf, P. Knirsch, A. Smola and C.J.C. Burges, Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces. DAGM Symposium Mustererkennung, Springer Lecture Notes in Computer Science, 1998, 8 pages

B. Schölkopf, A. Smola, K.-R. Müller, C.J.C. Burges and V. Vapnik, Support Vector methods in learning and feature extraction. Australian Journal of Intelligent Information Processing Systems, Vol. 1, pp. 3-9, 1998, 7 pages

C.J.C. Burges and B. Schölkopf, Improving the Accuracy and Speed of Support Vector Machines. Advances in Neural Information Processing Systems, 1997, 7 pages

B. Schölkopf, K. Sung, C.J.C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers. IEEE Transactions on Signal Processing, 45:11, pp 2758-2765, 1997, 7 pages

H. Drucker, C.J.C. Burges, L. Kaufman, A. Smola and V. Vapnik, Support Vector Regression Machines. Advances in Neural Information Processing Systems, 1997

C.J.C. Burges, Simplified Support Vector Decision Rules. 13th International Conference on Machine Learning, 1996, p. 71, 8 pages

B. Schölkopf, C.J.C. Burges, and V. Vapnik, Incorporating Invariances in Support Vector Learning Machines. In Proceedings, International Conference on Artificial Neural Networks, Springer Verlag, Berlin, 1996, 6 pages

V. Blanz, B. Schölkopf, H. Bultoff, C.J.C. Burges, V. Vapnik, and T. Vetter, Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models. International Conference on Artificial Neural Networks, 1996, 6 pages

B. Schölkopf, C.J.C. Burges, and V. Vapnik, Extracting Support Data for a Given Task. In Fayyad, U. M., Uthurusamy, R. (eds.), Proceedings, First International Conference on Knowledge Discovery & Data Mining, AAAI Press, Menlo Park, CA 1995, 6 pages

V. Vapnik, C.J.C. Burges, B. Schölkopf and R. Lyons, A New Method for Constructing Artificial Neural Networks. Interim ARPA Technical Report. AT&T Bell Laboratories, May 1, 1995.

Y. Bengio, Y. LeCun, C. Nohl and C.J.C. Burges, LeRec: a Neural Network / HMM Hybrid for On-Line Handwriting Recognition. Neural Computation, Vol. 7, Number 6, pp. 1289-1303, 1995.

C.J.C. Burges, Handwritten Digit String Recognition. In the Handbook of Brain Theory and Neural Networks, MIT Press, Ed. M. Arbib, 1995.

H.P. Graf, C.J.C. Burges, E. Cosatto and C.R. Nohl, Analysis of Complex and Noisy Check Images. IEEE International Conference on Image Processing, 1995.

J.S. Denker and C.J.C. Burges, Image Segmentation and Recognition. The Mathematics of Generalization: Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning, Addison Wesley, 1994, 24 pages

L.D. Jackel, M.Y. Battista, J. Ben, J. Bromley, C.J.C. Burges, H.S. Baird, E. Cosatto, J.S> Denker, H.P. Graf, H.P. Katseff, Y. LeCun, C.R. Nohl, E. Sackinger, J.H. Shamilian, T. Shoemaker, C.E. Stenard, B.I. Strom, R. Ting, T. Wood and C.R. Zuraw, Neural Net Applications in Character Recognition and Document Analysis. Neural Networks in Telecommunications, Kluwer, 1994, 15 pages

C.J.C. Burges, J.I. Ben, J.S. Denker, Y. LeCun and C.R. Nohl, Off Line Recognition of Handwritten Postal Words Using Neural Networks. International Journal of Pattern Recognition and Artificial Intelligence, 1993.

C.J.C. Burges, O. Matan, Y. LeCun, J.S. Denker, L.D. Jackel, C.E. Stenard, C.R. Nohl, J.I. Ben, Shortest Path Segmentation: A Method For Training a Neural Network to Recognize Character Strings. IJCNN Conference Proceedings, Vol. 3, pp 165-172, 1992, 8 pages

C.J.C. Burges, J.I. Ben and C.R. Nohl, Recognition of Handwritten Cursive Postal Words Using Neural Networks. USPS Advanced Technology Conference, pp. A-117-124, 1992.

H.P. Graf, J.I. Ben, C.J.C. Burges and C.R. Nohl, Address Block Location and Image Preprocessing Using Neural Net Hardware. USPS Advanced Technology Conference, pp. A-125–135, 1992.

C.R. Nohl, C.J.C. Burges and J.I. Ben, Character-Based Handwritten Address Word Recognition with Lexicon. USPS Advanced Technology Conference, pp 167-180, 1992.

O. Matan, C.J.C. Burges, Y. Le Cun and J.S. Denker, Multi-Digit Recognition Using a Space Displacement Neural Network. NIPS 4, pp 488-495, 1992.

O. Matan, H.S. Baird, J. Bromley, C.J.C. Burges, J.S. Denker, L.D. Jackel, Y. LeCun, E.P.D. Pednault, W.D. Satterfield, C.E. Stenard and T.J. Thompson, Reading Handwritten Digits: A ZIP Code Recognition System. IEEE Computer, Vol 25, Number 7, pp 59-63, 1992.