Data Science and Machine Learning Group

Home Research Publications

  • Wang, J., Xu, C., Yang, X. and Zurada, J. (2017). A Novel Pruning Algorithm for Smoothing Feed-forward Neural Networks based on Group Lasso. IEEE Transactions on Neural Networks and Learning Systems. Accepted.
  • Zou, B., Tang, Y., Xu, C., Xu, J. and You, X. (2017). K-Times Markov Sampling for SVMC. IEEE Transactions on Neural Networks and Learning Systems. Accepted.

  • [N. Denis, M. Fraser, B. Paget, Novel measure of regional homogeneity for fMRI data analysis. Poster at University of Ottawa Brain Health Research Day (2017).]
  • M. Fraser, Multi-step learning and underlying structure in statistical models. NIPS 2016.
  • [N. Denis, M. Fraser, In search of dynamic representations of state of mind: exploring mathematical methodologies for capturing higher-order responses in subjects listening to auditory narrative under fMRI. Poster in ``Topological, Geometric and Statistical Techniques in Biological Data Analysis" - workshop at Mathematical Biosciences Institute, Ohio State University (2016).]
  • Xu, C., Zhang, Y., Li, R. and Wu, X. (2016). On the Feasibility of Distributed Kernel Regression for Big Data. IEEE Transactions on Knowledge and Data Engineering, 28, 3041 - 3052.
  • H. Duan, V. Pestov and V. Singla, Text categorization via similarity search: an efficient and effective novel algorithm. - Similarity Search and Applications (SISAP 2013). Proceedings of the 6th International Conference. A Coruña, Spain, Oct. 2013. Springer Lecture Notes in Computer Science 8199, pp. 182-193. doi>10.1007/978-3-642-41062-8_19. [arXiv version]
  • A. Stojmirović, P. Andreae, M. Boland, T. W. Jordan and V. G. Pestov, PFMFind: a system for discovery of peptide homology and function. - Similarity Search and Applications (SISAP 2013). Proceedings of the 6th International Conference. A Coruña, Spain, Oct. 2013. Springer Lecture Notes in Computer Science 8199, pp. 319-324. doi>10.1007/978-3-642-41062-8_32. [arxiv version]
  • H. Duan, Bounding the Fat Shattering Dimension of a Composition Function Class Built Using a Continuous Logic Connective - The Waterloo Mathematics Review 2.1 (2012), pp. 4 - 20. Online version.
  • V. Pestov, Is the k-NN classifier in high dimensions affected by the curse of dimensionality? - Computers & Mathematics with Applications 65 (2013), pp. 1427--1437, doi> 10.1016/j.camwa.2012.09.011. [arXiv version]

  • V. Pestov, Lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions. - Algorithmica 66 (2013), 310-328. doi> 10.1007/s00453-012-9638-2. [arXiv]

  • V. Pestov, PAC learnability under non-atomic measures: a problem by Vidyasagar. - Theoretical Computer Science 473 (2013), 29-45. doi> 10.1016/j.tcs.2012.10.015. [arXiv]

  • Gonzalo Navarro and Vladimir Pestov, editors. Similarity Search and Applications: 5th International Conference, SISAP 2012, Toronto, ON, Canada, August 9-10, 2012, Proceedings, Springer Lecture Notes in Computer Science 7404, 2012, 255 pages, ISBN-13: 978-3642321528, doi > 10.1007/978-3-642-32153-5. The book webpage.
  • Damjan Kalajdzievski, Measurability Aspects of the Compactness Theorem for Sample Compression Schemes, M.Sc. thesis, University of Ottawa, 2012, 64 pp., arXiv.
  • V. Pestov, Indexability, concentration, and VC theory. - J. Discrete Algorithms 13 (2012), pp. 2-18. doi> 10.1016/j.jda.2011.10.002 [arXiv]

  • V. Pestov, PAC learnability versus VC dimension: a footnote to a basic result of statistical learning, in: Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN'2011), San José, CA (July 31 - Aug. 5, 2011), pp. 1141 - 1145, doi> 10.1109/IJCNN.2011.6033352. [arXiv]
  • V. Pestov, Lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions. - Proceedings of the 4th International Conference on Similarity Search and Applications (SISAP 2011), 30 June - 1 July 2011, Lipari, Sicily, Italy. Editor: Alfredo Ferro, ACM, New York, NY, 2011, pp. 25-32.
  • V. Pestov, A note on sample complexity of learning binary output neural networks under fixed input distributions. - in: Proc. 2010 Eleventh Brazilian Symposium on Neural Networks (São Bernardo do Campo, SP, Brazil, 23-28 October 2010), IEEE Computer Society, Los Alamitos-Washington-Tokyo, 2010, pp. 7-12. doi> 10.1109/SBRN.2010.10 [ arXiv ]
  • V. Pestov, PAC learnability of a concept class under non-atomic measures: a problem by Vidyasagar. - in: Proc. 21st Intern. Conference on Algorithmic Learning Theory (ALT'2010), Canberra, Australia, 6-8 Oct. 2010 (M. Hutter, F. Stephan, V. Vovk, T. Zuegmann, eds.), Lect. Notes in Artificial Intelligence 6331, Springer, 2010, pp. 134-147. [arXiv version]
  • V. Pestov, Intrinsic Dimensionality. - The SIGSPATIAL Special, Newsletter of the Association for Computer Machinery special interest group on spatial information, a special issue on searching in metric spaces, vol. 2, No. 2 (2010), 8-11.
  • V. Pestov, Indexability, concentration, and VC theory. - An invited paper, Proceedings of the 3rd International Conference on Similarity Search and Applications (SISAP 2010), 18-19 September 2010, Istanbul, Turkey. Editors: Paolo Ciaccia and Marco Patella, ACM, New York, NY, 2010, pp. 3-12.
  • V. Pestov, Predictive PAC learnability: a paradigm for learning from exchangeable input data. - In: Proc. 2010 IEEE Int. Conference on Granular Computing (San Jose, CA, 14-16 Aug. 2010), pp. 387-391, Symposium on Foundations and Practice of Data Mining. doi> 10.1109/GrC.2010.102 [arXiv]
  • Ilya Volnyansky and V. Pestov, Curse of dimensionality in pivot-based indexes. - Proceedings of the 2nd International Workshop on Similarity Search and Applications (SISAP 2009), Prague, Czech Republic, August 29-30, 2009, T. Skopal and P. Zezula (eds.), IEEE Computer Society, Los Alamitos--Washington--Tokyo, 2009, pp. 39-46. [arXiv version]
  • Ilya Volnyansky, Curse of Dimensionality in the Application of Pivot-based Indexes to the Similarity Search Problem, M.Sc. thesis, University of Ottawa, 2009, 56 pages, arXiv.
  • V. Pestov, An axiomatic approach to intrinsic dimension of a dataset. - Neural Networks 21, 2-3 (2008), 204-213. (A special volume on Advances in Neural Networks Research: IJCNN ′07, 2007 International Joint Conference on Neural Networks IJCNN ′07.) [arXiv version]
  • A. Stojmirović, V. Pestov, Indexing schemes for similarity search in datasets of short protein fragments. - Information Systems 32 (2007), 1145-1165.
  • V. Pestov, Intrinsic dimension of a dataset: what properties does one expect? - In: Proceedings of the 20th International Joint Conference on Neural Networks (IJCNN'2007), Orlando, Florida (Aug. 12--17, 2007), pp. 1775--1780. [arXiv version]