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NTIS 바로가기Neurocomputing, v.237, 2017년, pp.350 - 361
Zhou, Lina (Information Systems Department, UMBC, Baltimore, MD 21250, United States) , Pan, Shimei (Information Systems Department, UMBC, Baltimore, MD 21250, United States) , Wang, Jianwu (Information Systems Department, UMBC, Baltimore, MD 21250, United States) , Vasilakos, Athanasios V. (Department of Computer Science, Electrical and Space Engineering, Luleå)
Abstract Machine learning (ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the forefront in recent years partly owing to the advent of big data. ML algorithms have never been better promised while challenged by big data. Big data enables ML algorithms...
Science Jordan 349 255 2015 10.1126/science.aaa8415 Machine learning: trends, perspectives, and prospects
J. Big Data Tsai 2 1 2015 10.1186/s40537-015-0030-3 Big data analytics: a survey
J. Big Data Najafabadi 2 1 2015 10.1186/s40537-014-0007-7 Deep learning applications and challenges in big data analytics
Japkowicz 2011 Evaluating Learning Algorithms: a Classification Perspective
Russell 2010 Artificial Intelligence: A Modern Approach
IEEE Trans. on Pattern Anal. Mach. Intell., Trans. Bengio 35 1798 2013 10.1109/TPAMI.2013.50 Representation learning: a review and new perspectives
Dekel 377 2008 NIPS From Online to Batch Learning with Cutoff-Averaging
AI Mag. Amershi 35 105 2014 Power to the people: the role of humans in Interactive machine learning
Expert Syst. Mirchevska 31 163 2014 10.1111/exsy.12019 Combining domain knowledge and machine learning for robust fall detection
Yu 2007 Computing Sciences Incorporating Prior Domain Knowledge into Inductive Machine Learning
ACM Trans. Knowl. Discov. Data Rakthanmanon 7 10 2013 10.1145/2500489 Addressing Big data time series: mining Trillions of time series subsequences Under dynamic time Warping
Int. J. Inf. Manag. Gandomi 35 137 2015 10.1016/j.ijinfomgt.2014.10.007 Beyond the hype: Big data concepts, methods, and analytics
X.Cai, F.Nie, H.Huang, Multi-view K-means clustering on big data, in: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013, pp. 2598-2604.
10.1002/widm.1173 S. Ramírez-Gallego, S. García, H. Mouriño-Talín, D. Martínez-Rego, V. Bolón-Canedo, A. Alonso-Betanzos, et al., "Data discretization: taxonomy and big data challenge," Wiley Interdisciplinary Reviews, Data Mining and Knowledge Discovery, vol. 6, pp. 5-21, 2016.
Y.Z.Y.-M.Cheung, Discretizing Numerical Attributes in Decision Tree for Big Data Analysis, in: Proceedings of the 2014 IEEE International Conference on Data Mining Workshop (ICDMW), 2014.
Science Lake 350 1332 2015 10.1126/science.aab3050 Human-level concept learning through probabilistic program induction
J. Suzuki, H. Isozaki, and M. Nagata, Learning condensed feature representations from large unsupervised data sets for supervised learning, in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Human Language Technologies, short papers, 2, 2011, pp. 636-641.
Proc. VLDB Endow. Mozafari 8 125 2014 10.14778/2735471.2735474 Scaling up crowd-sourcing to very large datasets: a case for active learning
Clust. Comput. Su 17 1081 2014 10.1007/s10586-014-0360-5 Effective and efficient data sampling using bitmap indices
Appl. Soft Comput. Bolón-Canedo 30 136 2015 10.1016/j.asoc.2015.01.035 Distributed feature selection
Inf. Fusion Sun 26 36 2015 10.1016/j.inffus.2015.03.001 A review of Nyström methods for large-scale machine learning
J. Mach. Learn. Res. Tan 15 1371 2014 Towards ultrahigh dimensional feature selection for big data
10.1007/11925231_54 J. Cervantes, X. Li, W. Yu, Support vector machine classification based on fuzzy clustering for large data sets, in: Proceedings of the 5th MICAI, 2015, pp. 572-582.
10.1109/ICDCSW.2014.14 O. Y. S. Al-Jarrah, A., M. Elsalamouny, P. D. Yoo, S. Muhaidat, and K. Kim, Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection, in: Proceedings of the 2014 IEEE 34th International Conference on in Distributed Computing Systems Workshops (ICDCSW).
Soft Comput. - A Fusion Found., Methodol. Appl. Azar 19 1115 2015 Dimensionality reduction of medical big data using neural-fuzzy classifier
J. Mach. Learn. Res. Vincent 11 3371 2010 Stacked denoising Autoencoders: learning useful representations in a deep network with a local denoising criterion
Neurocomputing Liou 139 84 2014 10.1016/j.neucom.2013.09.055 Autoencoder for words
Proc. 23rd Int. Conf. Mach. Learn. Collobert 201 2006 Trading convexity for scalability
Bengio 2007 Large Scale Kernel Machines Scaling learning algorithms towards, AI
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, et al., "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems," CoRR, 2016.
J. Parallel Distrib. Comput. You 76 16 2015 10.1016/j.jpdc.2014.09.005 Scaling support vector machines on modern HPC platforms
Proc. VLDB Endow. Panda 2 1426 2009 10.14778/1687553.1687569 PLANET: massively parallel learning of tree ensembles with MapReduce
IEEE Trans. Big Data Xing 49 2015 10.1109/TBDATA.2015.2472014 Petuum: a new platform for distributed machine learning on Big data
R. Collobert, K. Kavukcuoglu, and C. Farabet, Torch7: A Matlab-like Environment for Machine Learning, in: Proceedings of the Neural Information Processing Systems (NIPS) Workshop on BigLearn, 2011.
W. Xu, Towards Optimal one pass large scale learning with averaged stochastic gradient descent, 2011. Available at: arXiv:1107.2490.
Neurocomputing Yue 219 364 2017 10.1016/j.neucom.2016.09.042 A data-intensive approach for discovering user similarities in social behavioral interactions based on the bayesian network
A. Kumar, A. Beutel, Q. Ho, E.P. Xing, Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data, in: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), Reykjavik, Iceland, 2014, pp. 531-539.
Sankar 2015 Fast Data Processing with Spark
Owen 2011 Mahout in Action
NIPS Chu 281 2006 Map-reduce for machine learning on multicore
IEEE Data Eng. Bull. Borkar 35 24 2012 Declarative systems for large-scale machine learning
Proc. VLDB Endow. Low 5 716 2012 10.14778/2212351.2212354 Distributed GraphLab: a framework for machine learning and data mining in the cloud
Theano Development Team, Theano: A Python framework for fast computation of mathematical expression. Available: arXiv:1605.02688.
IEEE Trans. Pattern Anal. Mach. Intell. Dong 27 603 2005 10.1109/TPAMI.2005.77 Fast SVM training algorithm with decomposition on very large data sets
J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, et al., Large scale distributed deep networks, in: Proceedings of the Neural Information Processing Systems, Lake Tahoe, Nevada, United States, 2012, pp. 1232-1240.
Mason 2016 Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force
Q.V.Le, J.Ngiam, A.Coates, A.Lahiri, B.Prochnow, A.Y.Ng, On optimization methods for deep learning, in: Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA, 2011.
Proc. Third Workshop Large Scale Data Min.: Theory Appl. Ganjisaffar 2 2011 Distributed tuning of machine learning algorithms using MapReduce Clusters
C.Dijun Luo, Ding, H.Huang, Parallelization with ultiplicative algorithms for big data mining, in: Proceedings of the 12th International Conference on Data Mining (ICDM), 2012, pp. 489-498.
Neurocomputing Triguero 150 331 2015 10.1016/j.neucom.2014.04.078 MRPR: A MapReduce solution for prototype reduction in big data classification
J. Big Data Landset 2 1 2015 10.1186/s40537-015-0032-1 A survey of open source tools for machine learning with big data in the Hadoop ecosystem
Hsu 2011 Scaling up machine learning: Parallel and distributed approaches Parallel online learning
P.Domingos, G.Hulten, A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering, presented at Proceedings of the Eighteenth International Conference on Machine Learning, 2001, pp. 106-113.
2012 Scaling up Machine Learning: Parallel and Distributed Approaches
Proc. 1st Int. Workshop Big Data, Streams Heterog. Source Min.: Algorithms, Syst., Program. Models Appl. Parker 1 2012 Unexpected challenges in large scale machine learning
Prog. Artif. Intell. Peteiro-Barral 2 1 2013 10.1007/s13748-012-0035-5 A survey of methods for distributed machine learning
K.L.C.Zhu, M.Savvides, Distributed class dependent feature analysis - A big data approach, in: proceedings of the 2014 IEEE International Conference on Big Data, 2014.
IEEE Int. Congr. Big Data (BigData Congr.) Yui 1 2013 A database-Hadoop hybrid approach to Scalable machine learning
Soft Comput. Çatak 1 2015 Classification with boosting of extreme learning machine over arbitrarily partitioned data
IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. Cavallaro 8 4634 2015 10.1109/JSTARS.2015.2458855 On Understanding Big data impacts in remotely sensed image classification using support vector machine methods
J.Zhu, J.Chen, W.Hu, Big Learning with Bayesian Methods. Available: 〈http://arxiv.org/pdf/1411.6370〉, 2014.
L.Bagheri, H.Goote, A.Hasan, G.Hazard, Risk adjustment of patient expenditures: A big data analytics approach, in Proceedings of the 2013 IEEE International Conference on Big Data, 2013.
Imagen. Classif. Deep convolutional Neural Netw. Krizhevsky 2012
Vis. Sci. Soc. Deng 1 2009 Construction and analysis of a large scale image ontology
Neurocomputing Guo 187 27 2016 10.1016/j.neucom.2015.09.116 Deep learning for visual understanding: a review
Neurocomputing Jiang 185 163 2016 10.1016/j.neucom.2015.12.042 Speed up deep neural network based pedestrian detection by sharing features across multi-scale models
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. Manning, A. Ng, et al., Recursive deep models for semantic compositionality over a sentiment treebank, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013.
Neurocomputing Zhou 120 536 2013 10.1016/j.neucom.2013.04.017 Active deep learning method for semi-supervised sentiment classification
Cogn. Comput. Zeng 8 684 2016 10.1007/s12559-016-9404-x Deep belief networks for quantitative analysis of a gold immunochromatographic strip
Goodfellow 2016 Deep Learning
The J. Mach. Learn. Res. Erhan 11 625 2010 Why does Unsupervised Pre-training help deep learning?
T.Mikolov, I.Sutskever, K.Chen, G.S.Corrado, J.Dean, Distributed Representations of Words and Phrases and their Compositionality, presented at the NIPS, Stateline, NV, 2013.
Access, IEEE Chen 2 514 2014 10.1109/ACCESS.2014.2325029 Big data deep learning: challenges and perspectives
47th Annu. IEEE/ACM Int. Symp. Micro. Chen 609 2014 DaDianNao: a machine-learning Supercomputer
IEEE Int. Symp. High. Perform. Comput. Archit. (HPCA) Mahajan 14 2016 TABLA: a unified template-based framework for accelerating statistical machine learning
M.Zaharia, M.Chowdhury, M.J.Franklin, S.Shenker, I.Stoica, Spark: cluster computing with working sets, presented at in: Proceedings of the 2nd USENIX conference on Hot topics in Cloud Computing, Boston, MA, 2010.
E.Bortnikov, A.Frank, E.Hillel, S.Rao, Predicting execution bottlenecks in map-reduce clusters, in: Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing, 2012, pp. 18-18.
Knowledge Inf. Syst. Vaidya 14 161 2008 10.1007/s10115-007-0073-7 Privacy-preserving SVM classification
Proc. VLDB Endow. Popescu 6 1678 2013 10.14778/2556549.2556553 PREDIcT: towards predicting the runtime of large scale iterative analytics
Big Data Anal. Bioinforma.: A Mach. Learn. Perspect. Kashyap 2015
J.Xu, C.Tekin, M.van der Schaar, Learning optimal classifier chains for real-time big data mining, in Proceedings 51st Annu. Allerton Conference Comm., Control and Comput. (Allerton'13), 2013.
J. Mach. Learn. Res. Lu 17 1 2016 Large scale online kernel learning
The J. Mach. Learn. Res. Wang 13 3103 2012 Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training
IEEE Comput. Intell. Mag. Zhai 9 14 2014 10.1109/MCI.2014.2326099 The emerging big dimensionality
J. Big Data Singh 2 1 2014 A survey on platforms for big data analytics
T.Kraska, A.Talwalkar, J.Duchi, R.Griffith, M.J.Franklin, M.I.Jordan, MLbase: A Distributed Machine-learning System, in: Proceedings of the 6th Biennial Conference on Innovative Data Systems Research, Asilomar, California, USA, 2013.
Proc. VLDB Endow. Markl 7 1730 2014 10.14778/2733004.2733075 Breaking the chains: on declarative data analysis and data independence in the big data era
Tong 2016 2010
IEEE Autotestcon Armes, M 2013 Using Big data and predictive machine learning in aerospace test environments
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