.. ****************************************************************************** .. * Copyright 2019-2021 Intel Corporation .. * .. * Licensed under the Apache License, Version 2.0 (the "License"); .. * you may not use this file except in compliance with the License. .. * You may obtain a copy of the License at .. * .. * http://www.apache.org/licenses/LICENSE-2.0 .. * .. * Unless required by applicable law or agreed to in writing, software .. * distributed under the License is distributed on an "AS IS" BASIS, .. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. .. * See the License for the specific language governing permissions and .. * limitations under the License. .. *******************************************************************************/ .. _dg_bibliography: Bibliography ============ For more information about algorithms implemented in |short_name|, refer to the following publications: .. [Adams2003] Adams, Robert A., and John JF Fournier. Sobolev spaces. Vol. 140. Elsevier, 2003 .. [Agrawal94] Rakesh Agrawal, Ramakrishnan Srikant. *Fast Algorithms for Mining Association Rules*. Proceedings of the 20th VLDB Conference Santiago, Chile, 1994. .. [Arthur2007] 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. .. [Bahmani2012] 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. .. [Ben2005] 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. .. [Bentley80] J. L. Bentley. Multidimensional Divide and Conquer. Communications of the ACM, 23(4):214--229, 1980. .. [Billor2000] Nedret Billor, Ali S. Hadib, and Paul F. Velleman. BACON: blocked adaptive computationally efficient outlier nominators. Computational Statistics & Data Analysis, 34, 279-298, 2000. .. [Bishop2006] 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. .. [Boser92] 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. .. [Breiman2001] Leo Breiman. *Random Forests*. Machine Learning, Volume 45 Issue 1, pp. 5-32, 2001. .. [Breiman84] Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone. *Classification and Regression Trees*. Chapman & Hall, 1984. .. [Bro07] Bro, R.; Acar, E.; Kolda, T.. *Resolving the sign ambiguity in the singular value decomposition*. SANDIA Report, SAND2007-6422, Unlimited Release, October, 2007. .. [Byrd2015] 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. .. [Chen2016] 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. .. [Carletti2021] 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. .. [Defazio2014] 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. .. [Demmel90] J. W. Demmel and W. Kahan. *Accurate singular values of bidiagonal matrices*. SIAM J. Sci. Stat. Comput., 11 (1990), pp. 873-912. .. [Dempster77] A.P.Dempster, N.M. Laird, and D.B. Rubin. *Maximum-likelihood from incomplete data via the em algorithm*. J. Royal Statist. Soc. Ser. B., 39, 1977. .. [Duchi2011] 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. .. [Ester96] 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. .. [Fan05] 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. .. [Fleischer2008] Rudolf Fleischer, Jinhui Xu. Algorithmic Aspects in Information and Management. 4th International conference, AAIM 2008, Shanghai, China, June 23-25, 2008. Proceedings, Springer. .. [Freund99] 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. .. [Friedman98] Friedman, Jerome H., Trevor J. Hastie and Robert Tibshirani. *Additive Logistic Regression: a Statistical View of Boosting.*. 1998. .. [Friedman00] 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. .. [Friedman2010] Friedman, Jerome, Trevor Hastie, and Rob Tibshirani. *Regularization paths for generalized linear models via coordinate descent.*. Journal of statistical software 33.1 (2010): 1. .. [Friedman2017] Jerome Friedman, Trevor Hastie, Robert Tibshirani. 2017. *The Elements of Statistical Learning Data Mining, Inference, and Prediction.* Springer. .. [Freund01] Yoav Freund. An adaptive version of the boost by majority algorithm. Machine Learning (43), pp. 293-318, 2001. .. [Gross2014] J. Gross, J. Yellen, P. Zhang, Handbook of Graph Theory, Second Edition, 2014. .. [Hastie2009] Trevor Hastie, Robert Tibshirani, Jerome Friedman. *The Elements of Statistical Learning: Data Mining, Inference, and Prediction*. Second Edition (Springer Series in Statistics), Springer, 2009. Corr. 7th printing 2013 edition (December 23, 2011). .. [Hoerl70] Arthur E. Hoerl and Robert W. Kennard. *Ridge Regression: Biased Estimation for Nonorthogonal Problems*. Technometrics, Vol. 12, No. 1 (Feb., 1970), pp. 55-67. .. [Hsu02] 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. .. [Hu2008] Yifan Hu, Yehuda Koren, Chris Volinsky. Collaborative Filtering for Implicit Feedback Datasets. ICDM'08. Eighth IEEE International Conference, 2008. .. [James2013] 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 6\ :sup:`th` printing 2015). .. [Joachims99] 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. .. [Lang87] S. Lang. *Linear Algebra*. Springer-Verlag New York, 1987. .. [Li2015] 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. .. [Lloyd82] Stuart P Lloyd. *Least squares quantization in PCM*. IEEE Transactions on Information Theory 1982, 28 (2): 1982pp: 129–137. .. [Matsumoto98] 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. .. [Matsumoto2000] Matsumoto, M., Nishimura, T. Dynamic Creation of Pseudorandom Number Generators Monte Carlo and Quasi-Monte Carlo Methods 1998, Ed. Niederreiter, H. and Spanier, J., Springer 2000, pp. 56-69, available from http://www.math.sci.hiroshima-u.ac.jp/%7Em-mat/MT/DC/dc.html. .. [Mitchell97] Tom M. Mitchell. *Machine Learning*. McGraw-Hill Education, 1997. .. [Mu2014] 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. .. [OpenCLSpec] Khronos OpenCL Working Group, The OpenCL Specification Version:2.1 Document Revision:24 Available from `opencl-2.1.pdf `_ .. [Patwary2016] 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. .. [Ping14] Ping Tak Peter and Eric Polizzi. *FEAST as a Subspace Iteration Eigensolver Accelerated by Approximate Spectral Projection.* 2014. .. [Platt98] 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. .. [Quinlan86] J. R. Quinlan. *Induction of Decision Trees*. Machine Learning, Volume 1 Issue 1, pp. 81-106, 1986. .. [Quinlan87] J. R. Quinlan. *Simplifying decision trees*. International journal of Man-Machine Studies, Volume 27 Issue 3, pp. 221-234, 1987. .. [Renie03] 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. .. [Rumelhart86] David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams. *Learning representations by back-propagating errors*. Nature (323), pp. 533-536, 1986. .. [Sokolova09] 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. .. [SYCLSpec] Khronos®OpenCL™ Working Group --- SYCL™ subgroup, SYCL™ Specification SYCL™ integrates OpenCL™ devices with modern C++, Version 1.2.1 Available from `sycl-1.2.1.pdf `_ .. [Sutton2018] Michael Sutton, Tal Ben-Nun, Amnon Barak. *Optimizing Parallel Graph Connectivity Computation via Subgraph Sampling*. Symposium on Parallel and Distributed Processing, IPDPS 2018. .. [Tan2005] 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. .. [Verma2014] 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. .. [Wen2018] 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). .. [Wu04] 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. .. [Zhu2005] Zhu, Ji, Hui Zou, Saharon Rosset and Trevor J. Hastie. *Multi-class AdaBoost*. 2005