Developer Guide and Reference

  • 2021.2
  • 03/26/2021
  • Public Content
Contents

Bibliography

For more information about algorithms implemented in oneDAL, 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.
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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.
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Breiman2001
Leo Breiman.
Random Forests
. Machine Learning, Volume 45 Issue 1, pp. 5-32, 2001.
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Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone.
Classification and Regression Trees
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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.
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.
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J. W. Demmel and W. Kahan.
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A.P.Dempster, N.M. Laird, and D.B. Rubin.
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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.
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
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
  1. Lang.
    Linear Algebra
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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
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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
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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.
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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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J. R. Quinlan.
Simplifying decision trees
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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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David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams.
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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.
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
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

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.