COMP 875 Presentation Reading Lists

• Kobus Barnard and David Forsyth, Learning the Semantics of Words and Pictures, International Conference on Computer Vision, vol 2, pp. 408-415, 2001.
  
  
• Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik Learned-Miller, David A. Forsythm, Names and Faces in the News, Computer Vision and Pattern Recognition (CVPR), 2004.
  
  

• Eric Brill, A Report of Recent Progress in Transformation-Based Error-Driven Learning, Technical report, 1994.
  
  
• Eric Brill, Unsupervised Learning of Disambiguation Rules for Part of Speech Tagging, Proceedings of the Third ACL Workshop on Very Large Corpora, 1995.
  
  
• Lance A. Ramshaw and Mitchell P. Marcus, Text Chunking using Transformation-Based Learning, Proceedings of the Third ACL Workshop on Very Large Corpora, 1995.
  
  

• Lawrence R. Rabiner, A tutorial on Hidden Markov Models and selected applications in speech recognition, In Proceedings of the IEEE, pages 257-286, 1989.
  
  
• Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, and Chris Watkins, Text Classification using String Kernels, 2(Feb):419-444, 2002.
  
  

• Charles Wright, Lucas Ballard, Scott Coulls, Fabian Monrose, and Gerald Masson, Spot me if you can: recovering spoken phrases in encrypted VoIP conversations, To appear in Proceedings of IEEE Symposium on Security and Privacy, May, 2008.
  
  
• Charles Wright, Fabian Monrose, and Gerald Masson, On Inferring Application Protocol Behaviors in Encrypted Network Traffic, In Journal of Machine Learning Research (JMLR): Special issue on Machine Learning for Computer Security, volume 7, 2745-2769, 2006.
  
  
• Song, D. X., Wagner, D., and Tian, X. 2001, Timing analysis of keystrokes and timing attacks on SSH, In Proceedings of the 10th Conference on USENIX Security Symposium - Volume 10 (Washington, D.C., August 13 - 17, 2001). USENIX Security Symposium. USENIX Association, Berkeley, CA, 25-25.
  
  

• Jeff Yan and Ahmad Salah El Ahmad, Breaking visual CAPTCHAs with Naive Pattern Recognition Algorithms, Annual Computer Security Applications Conference, 2007.
  
  
• Kumar Chellapilla and Patrice Simard, Using Machine Learning to Break Visual Human Interaction Proofs, Advances in Neural Information Processing Systems, 2004.
  
  
• Greg Mori and Jitendra Malik, Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA, Proc. of the Computer Vision and Pattern Recognition Conference, IEEE Computer Society, vol.1, pages:I-134 - I-141, June 18-20, 2003.
  
  
• Philippe Golle. Machine learning attacks against the Asirra CAPTCHA, ACM Conference on Computer and Communications Security, 2008.
  
  

• B. C. Davis, P. T. Fletcher, E. Bullitt, and S. C. Joshi, Population shape regression from random design data, In Proc. of Int. Conf. on Comp. Vision, 2007.
  
  
• B. C. Davis and S. Lazebnik, Analysis of human attractiveness using manifold kernel regression, In Proceedings of the IEEE International Conference on Image Processing (Special Sesson on Aesthetics, Mood, and Emotion), 2008.
  
  

• Jianbo Shi and Jitendra Malik, Normalized Cuts and Image Segmentation IEEE Transactions on Pattern Analysis and Machine Learning, VOL.22, No.8, August 2000.
  
  
• Maurizio Filippone, Francesco Camastra, Franceso Masulli and Stefano Rovetta, A suvey of kernel and spectral methods for clustering, Pattern Recognition 41 (2008) 176-190.
  
  

• N. M. Ball, R. J. Brunner, A. D. Myers, N. E. Strand, S. L. Alberts, and D. Tcheng, Robust machine learning applied to astronomical datasets III: Probabilistic photometric redshifts for galaxies and quasars in the SDSS and GALEX, The Astrophysical Journal 683 (2008) 12-21.
  
  
• Part II (pp. 38ff.) of N. D. Kumar, Machine learning techniques for astrophysical modelling and photometric redshift estimation of quasars in optical sky surveys, Master's dissertation, Oxford University Computing Laboratory, Oxford, UK, 116 pp; 2008.
  
  

• J. Jiang, S. Zheng, A. Toga, and Z. Tu, Learning Based Coarse-to-fine Image Registration, CVPR 2008.
  
  
• Z. Tu, Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering, ICCV 2005.
  
  P. Dollar, Z. Tu, and S. Belongie, Supervised learning of edges and object boundaries, CVPR 2005.
  
  

• M. Schultz, T. Joachims, Learning a Distance Metric from Relative Comparisons, NIPS 2004.
  
  
• K. Weinberger, J. Blitzer, L. Saul, Distance Metric Learning for Large Margin Nearest Neighbor Classification, NIPS 2006.
  
  
• A. Frome, Y. Singer, J. Malik, Image Retrieval and Classification Using Local Distance Functions, NIPS 2006.
  
  
• A. Frome, Y. Singer, F. Sha, J. Malik, Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification, ICCV 2007.
  
  

• A. Andoni and P. Indyk. Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions. Communications of the ACM, vol. 51, no. 1, 2008, p. 117-122.
  
  
• P. Jain, B. Kulis, and K. Grauman. Fast image search for learned metrics. In CVPR, 2008.
  
  
• A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. In CVPR, 2008.
  
  
• Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, 2008.
  
  

• O. Chum, J. Philbin, and A. Zisserman, Near Duplicate Image Detection: min-Hash and tf-idf Weighting, Proceedings of the British Machine Vision Conference, 2008.
  
  
• O. Chum, M. Perd'och, and J. Matas, Geometric min-Hashing: Finding a (Thick) Needle in a Haystack, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2009.
  
  

• S. Kumar and M. Hebert, Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification, ICCV 2003.
  
  
• X. He, R. Zemel, and M. Carreira-Perpinan, Multiscale conditional random fields for image labelling, CVPR 2004.
  
  

• Y. Rubner, L. J. Guibas and C. Tomasi, The earth mover's distance, multi-dimensional scaling, and color-based image retrieval, Proceedings of the DARPA Image Understanding Workshop (1997).
  
  
• S. Shirdhonkar, D. W. Jacobs, Approximate earth mover's distance in linear time, IEEE Conference on Computer Vision and Pattern (2008).
  
  

• Lawrence CE, Altshul SF, Boguski MS, Liu JS, Neuwald AF, Wootton JC. Detecting subtle sequence signals: A Gibbs sampling strategy for multiple aligment. Science. 1993. 262: 208-214.
  
  
• Neuwald AF, Liu JS, Lawrence CE. Gibbs Motif Sampling: Detection of Bacterial Outer Membrane Repeats. Protein Science. 1995, 4, 1618-1632.
  
  
• Rouchka EC. A Brief Overview of Gibbs Sampling. 1997.
  
  
• Thijs G, Marchal K, Lescot M, Rombauts S, De Moor B, Rouzi P, Moreau Y. A Gibbs Sampling Method to Detect Overrepresented Motifs in the Upstream Regions of Coexpressed Genes. RECOMB 2001. 5: 305-312.
  
  

• Schuldt, C., Laptev, I., and Caputo, B. Recognizing Human Actions: A Local SVM Approach. In Proceedings of the 17th International Conference on Pattern Recognition, 2004.
  
  
• Y. Ke, R. Sukthankar, and M. Hebert. Event Detection in Cluttered Videos. ICCV, 2007.
  
  
• Juan Carlos Niebles, Hongcheng Wang and Li Fei-Fei. Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words. International Journal of Computer Vision (IJCV). September, 2008.
  
  

• T. Lee and J. J. Mody, Behavioral Classification, In EICAR Conference, 2006.
  
  
• Konrad Rieck, Thorsten Holz, Carsten Willems, Patrick Dussel, Pavel Laskov, Learning and Classification of Malware Behavior. In Fifth Conference on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA 08).
  
  
• Ulrich Bayer, Paolo Milani Comparetti, Clemens Hlauschek, Christopher Kruegel, and Engin Kirda, Scalable, Behavior-Based Malware Clustering, In Proceedings of the Network and Distributed System Security Symposium, San Diego, California, USA, February 2009.
  
  

• P. Abbeel and Y. Ng, Exploration and apprenticeship learning in reinforcement learning, ICML 2005.
  
  
• P. Abbeel, A. Coates, M. Quigley, and A. Ng, An Application of Reinforcement Learning to Aerobatic Helicopter Flight, NIPS 2007.
  
  
• A. Coates, P. Abbeel, and A. Ng, Learning for Control from Multiple Demonstrations, ICML 2008.
  
  
• M. Kearns and S. Singh, Near-optimal reinforcement learning in polynomial time, Machine Learning Journal, 2002.