Bibliography#

For more information about algorithms implemented in oneDAL, refer to the following publications:

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Adams, Robert A., and John JF Fournier. Sobolev spaces. Vol. 140. Elsevier, 2003

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Rakesh Agrawal, Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. Proceedings of the 20th VLDB Conference Santiago, Chile, 1994.

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Arthur, D., Vassilvitskii, S. k-means++: The Advantages of Careful Seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics Philadelphia, PA, USA, 2007, pp. 1027-1035. Available from http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf.

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B. Bahmani, B. Moseley, A. Vattani, R. Kumar, S. Vassilvitskii. Scalable K-means++. Proceedings of the VLDB Endowment, 2012. Available from http://vldb.org/pvldb/vol5/p622_bahmanbahmani_vldb2012.pdf.

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Ben-Gal I. Outlier detection. In: Maimon O. and Rockach L. (Eds.) Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers”, Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.

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Nedret Billor, Ali S. Hadib, and Paul F. Velleman. BACON: blocked adaptive computationally efficient outlier nominators. Computational Statistics & Data Analysis, 34, 279-298, 2000.

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Christopher M. Bishop. Pattern Recognition and Machine Learning, p.198, Computational Statistics & Data Analysis, 34, 279-298, 2000. Springer Science+Business Media, LLC, ISBN-10: 0-387-31073-8, 2006.

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B. E. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal marginclassifiers.. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp: 144–152, ACM Press, 1992.

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Bro, R.; Acar, E.; Kolda, T.. Resolving the sign ambiguity in the singular value decomposition. SANDIA Report, SAND2007-6422, Unlimited Release, October, 2007.

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R. H. Byrd, S. L. Hansen, Jorge Nocedal, Y. Singer. A Stochastic Quasi-Newton Method for Large-Scale Optimization, 2015. arXiv:1401.7020v2 [math.OC]. Available from http://arxiv.org/abs/1401.7020v2.

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T. Chen, C. Guestrin. XGBoost: A Scalable Tree Boosting System, KDD ‘16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

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Carletti, Vincenzo, et al. Parallel Subgraph Isomorphism on Multi-core Architectures: A Comparison of Four Strategies Based on Tree Search. Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). Springer, Cham, 2021.

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Defazio, Aaron, Francis Bach, and Simon Lacoste-Julien. SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives. Advances in neural information processing systems. 2014.

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J. W. Demmel and W. Kahan. Accurate singular values of bidiagonal matrices. SIAM J. Sci. Stat. Comput., 11 (1990), pp. 873-912.

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Elad Hazan, John Duchi, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:21212159, 2011.

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Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. A density-based algorithm for discovering clusters in large spatial databases with noise.. In Proceedings of the 2nd ACM International Conference on Knowledge Discovery and Data Mining (KDD). 226-231, 1996.

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Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin. Working Set Selection Using Second Order Information for Training Support Vector Machines.. Journal of Machine Learning Research 6 (2005), pp: 1889–1918.

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Rudolf Fleischer, Jinhui Xu. Algorithmic Aspects in Information and Management. 4th International conference, AAIM 2008, Shanghai, China, June 23-25, 2008. Proceedings, Springer.

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Yoav Freund, Robert E. Schapire. Additive Logistic regression: a statistical view of boosting. Journal of Japanese Society for Artificial Intelligence (14(5)), 771-780, 1999.

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Friedman, Jerome H., Trevor J. Hastie and Robert Tibshirani. Additive Logistic Regression: a Statistical View of Boosting.. 1998.

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Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Additive Logistic regression: a statistical view of boosting. The Annals of Statistics, 28(2), pp: 337-407, 2000.

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Friedman, Jerome, Trevor Hastie, and Rob Tibshirani. Regularization paths for generalized linear models via coordinate descent.. Journal of statistical software 33.1 (2010): 1.

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Jerome Friedman, Trevor Hastie, Robert Tibshirani. 2017. The Elements of Statistical Learning Data Mining, Inference, and Prediction. Springer.

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Yoav Freund. An adaptive version of the boost by majority algorithm. Machine Learning (43), pp. 293-318, 2001.

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Chih-Wei Hsu and Chih-Jen Lin. A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp: 415-425, 2002.

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Yifan Hu, Yehuda Koren, Chris Volinsky. Collaborative Filtering for Implicit Feedback Datasets. ICDM’08. Eighth IEEE International Conference, 2008.

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Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer Series in Statistics, Springer, 2013 (Corrected at 6th printing 2015).

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Thorsten Joachims. Making Large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Schölkopf, C. Burges, and A. Smola (ed.), pp: 169 – 184, MIT Press Cambridge, MA, USA 1999.

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Li, Shengren, and Nina Amenta. “Brute-force k-nearest neighbors search on the GPU.” In International Conference on Similarity Search and Applications, pp. 259-270. Springer, Cham, 2015.

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Matsumoto, M., Nishimura, T. Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator. ACM Transactions on Modeling and Computer Simulation, Vol. 8, No. 1, pp. 3-30, January 1998.

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Mu Li, Tong Zhang, Yuqiang Chen, Alexander J. Smola. Efficient Mini-batch Training for Stochastic Optimization, 2014. Available from https://www.cs.cmu.edu/~muli/file/minibatch_sgd.pdf.

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Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram, Jialin Liu, Peter Sadowski, Evan Racah, Suren Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey. PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures, 2016. Available from https://arxiv.org/abs/1607.08220.

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Ping Tak Peter and Eric Polizzi. FEAST as a Subspace Iteration Eigensolver Accelerated by Approximate Spectral Projection. 2014.

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Platt, John. “Sequential minimal optimization: A fast algorithm for training support vector machines.” (1998). Available from https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf.

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Jason D.M. Rennie, Lawrence, Shih, Jaime Teevan, David R. Karget. Tackling the Poor Assumptions of Naïve Bayes Text classifiers. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003.

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Marina Sokolova, Guy Lapalme. A systematic analysis of performance measures for classification tasks. Information Processing and Management 45 (2009), pp. 427–437. Available from http://atour.iro.umontreal.ca/rali/sites/default/files/publis/SokolovaLapalme-JIPM09.pdf.

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Michael Sutton, Tal Ben-Nun, Amnon Barak. Optimizing Parallel Graph Connectivity Computation via Subgraph Sampling. Symposium on Parallel and Distributed Processing, IPDPS 2018.

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Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, (First Edition) Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA, 2005, ISBN: 032132136.

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Verma, Deepika, Namita Kakkar, and Neha Mehan. “Comparison of brute-force and KD tree algorithm.” International Journal of Advanced Research in Computer and Communication Engineering 3, no. 1 (2014): 5291-5294.

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Wen, Zeyi, Jiashuai Shi, Qinbin Li, Bingsheng He, and Jian Chen. ThunderSVM: A fast SVM library on GPUs and CPUs. The Journal of Machine Learning Research, 19, 1-5 (2018).

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Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng. Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research 5, pp: 975-1005, 2004.

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Zhu, Ji, Hui Zou, Saharon Rosset and Trevor J. Hastie. Multi-class AdaBoost. 2005