ТОП просматриваемых книг сайта:
Computational Statistics in Data Science. Группа авторов
Читать онлайн.Название Computational Statistics in Data Science
Год выпуска 0
isbn 9781119561088
Автор произведения Группа авторов
Жанр Математика
Издательство John Wiley & Sons Limited
78 78 Gupta, A., Taneja, S.B., Malik, G. et al. (2019) SLANGZY: a fuzzy logic‐based algorithm for english slang meaning selection. Prog. Artif. Intell., 8, 111–121. doi: 10.1007/s13748‐018‐0159‐3.
79 79 Mehta, J.S. (2017) Concept drift in streaming data classification: algorithms, platforms and issues. Procedia Comput. Sci., 122, 804–811.
80 80 BakshiRohit, P. and Agarwal, S. (2016) Stream data mining: platforms, algorithms, performance evaluators and research trends. Int. J. Database Theory App., 9 (9), 201–218.
81 81 Wei, X., Liu, Y., and Wanga, X. (2019) A survey on quality‐assurance approximate stream processing and applications. Futur. Gener. Comput. Syst., 101, 1062–1080.
82 82 Hu, Y., Jiang, Z., Zhan, P. et al. (2018) A novel multi‐resolution representation for streaming time series. Procedia Comput. Sci., 129, 178–184. doi: 10.1016/j.procs.2018.03.069.
83 83 Yaseen, M.U., Anjum, A., Rana, O., and Hill, R. (2018) Cloud‐based scalable object detection and classification in video streams. Futur. Gener. Comput. Syst., 80, 286–298. doi: 10.1016/j.future.2017.02.003.
84 84 Boushaki, S.I., Kamel, N., and Bendjeghaba, O. (2018) High‐dimensional text datasets clustering algorithm based on cuckoo search and latent semantic indexing. J. Inf. Knowl. Manag., 17 (3), 1–24.
85 85 Neto, J.M., Severiano Junior, C.A., Guimarães, F.G. et al. (2020) Evolving clustering algorithm based on mixture of typicalities for stream. Futur. Gener. Comput. Syst., 106, 672–684.
86 86 Ibrahim, O.A., Du, Y., and Keller, J.M. (2018) Extended robust online streaming clustering (EROLSC), in Information Processing and Management of Uncertainty in Knowledge‐Based Systems: Theory and Foundations (eds J. Medina et al.), Springer, Cadiz.
87 87 Sharma, N., Masih, S., and Makhija, P. (2018) A survey on clustering algorithms for data streams. Int. J. Comput. Appl., 182 (22), 18–24.
88 88 Panagiotou, N., Katakis, I., and Gunopulos, D. (2016) Detecting events in online social networks: definitions, trends and challenges, in Solving Large Scale Learning Tasks: Challenges and Algorithms (ed. S. Michaelis), Springer, Cham, pp. 42–84.
89 89 Li, Y., Guo, L., and Zhou, Z. (2019) Towards safe weakly supervised learning. IEEE Trans. Pattern Anal. Mach. Intell., 43 (1), 334–346. doi: 10.1109/TPAMI.2019.2922396.
90 90 Le Nguyen, M.H., Gomes, H.M., and Bifet, A. (2019). Semi‐Supervised Learning Eover Streaming Data Using MOA. 2019 IEEE International Conference on Big Data (Big Data). IEEE, Los Angeles, CA, USA, pp. 553–562. doi: 10.1109/BigData47090.2019.9006217.
91 91 Zhu, Y. and Li, Y.‐F. (2020) Semi‐supervised streaming learning with emerging new labels. Proc. Thirty‐Fourth AAAI Conf. Artif. Intel., 34, 7015–7022. doi: 10.1609/aaai.v34i04.6186.
92 92 Li, P., Wu, X., Hu, X., and Wang, H. (2015) Learning concept‐drifting data streams with random ensemble decision trees. Neurocomputing, 166, 68–83.
93 93 Sethi, T.S. and Kantardzic, M. (2017) On the reliable detection of concept drift from streaming unlabeled data. Expert Syst. Appl., 82, 77–99. doi: 10.1016/j.eswa.2017.04.008.
94 94 Masud, M.M., Gao, J., Khan, L. et al. (2008) A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data. 2008 Eighth IEEE International Conference on Data Mining. IEEE, Pisa, pp. 929–934. doi: 10.1109/ICDM.2008.152.
95 95 BakshiRohit, P. and Agarwal, S. (2017) Critical parameter analysis of vertical hoeffding tree for optimized performance using SAMOA. Int. J. Mach. Learn. Cybern., 8, 1389–1402.
96 96 Ullah, A., Muhammad, K., Haq, I.U., and Baik, S.W. (2019) Action recognition using optimized deep autoencoder and CNN for surveillance data streams of non‐stationary environments. Futur. Gener. Comput. Syst., 96, 386–397. doi: 10.1016/j.future.2019.01.029.
97 97 Elsaleh, T., Enshaeifar, S., Rezvani, R. et al. (2020) IoT‐stream: a lightweight ontology for internet of things data streams and its use with data analytics and event detection services. Sensors (Basel), 20 (4), 953. doi: 10.3390/s20040953.
98 98 Janowicz, K., Haller, A., Cox, S.J. et al. (2019) SOSA: a lightweight ontology for sensors, observations, samples, and actuators. J. Web Semant., 56, 1–10. doi: 10.2139/ssrn.3248499.
99 99 Gonzalez‐Gil, P., Skarmeta, A.F., and Martinez, J.A. (2019) Towards an Ontology for IoT Context‐Based Security Evaluation. Proceedings of the 2019 Global IoT Summit (GIoTS), Aarhus, Denmark, pp. 1–6.
100 100 Bazoobandi, H.R., Beck, H., and Urbani, J. (2017) Towards expressive stream reasoning with laser, in The Semantic Web, vol. 10587 (ed. C.E. d'Amato), LNCS, pp. 87–103.
101 101 Albahri, O.S., Albahri, A.S., Mohammed, K.I. et al. (2018) Systematic review of real‐time remote health monitoring system in triage and priority‐based sensor technology: Taxonomy, open challenges, motivation and recommendations. J. Med. Syst., 42, 80. doi: 10.1007/s10916‐018‐0943‐4.
102 102 D'Aniello, G., Gaeta, M., and Orciuoli, F. (2018) An approach based on semantic stream reasoning to support decision processes in smart cities. Telemat. Inform., 35 (1), 68–81. doi: 10.1016/j.tele.2017.09.019.
103 103 Mondal, J. and Deshpande, A. (2018) Stream querying and reasoning on social data, in Encyclopedia of Social Network Analysis and Mining (eds R. Alhajj and J. Rokne), Springer, New York. doi: 10.1007/978‐1‐4939‐7131‐2_391.
104 104 Wen, Y., Zhang, Y., Huang, L. et al. (2019) Semantic modelling of ship behavior in harbor based on ontology and dynamic bayesian network. Int. J. Geogr. Inf. Sci., 8 (3), 107. doi: 10.3390/ijgi8030107.
105 105 Compton, M., Barnaghi, P., Bermudez, R.G. et al. (2012) The SSN ontology of the W3C semantic sensor network incubator group. J. Web Semant., 17, 25–32.
106 106 Daniele, L., den Hartog, F., and Roes, J. (2015) Created in close einteraction with the industry: the smart appliances reference (saref) ontology, in Formal Ontologies Meet Industries, vol. 225 (eds R. Cuel and R. Young), LNBIP, pp. 100–112. doi: 10.1007/978‐3‐319‐21545‐7_9.
107 107 Franka, M.T., Baderb, S., Simko, V., and Zander, S. (2018) LSane: collaborative validation and enrichment of heterogeneous observation streams. Procedia Comput. Sci., 137, 235–241. doi: 10.1016/j.procs.2018.09.022.
108 108 Kolozali, S., Bermudez‐Edo, M., Puschmann, D. et al. (2014) A knowledge‐Based Approach for Real‐Time IoT Data Stream Annotation and Processing. 2014 IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom). IEEE, Taipei, pp. 215–222. doi: 10.1109/iThings.2014.39.
109 109 Cardellini, V., Mencagli, G., Talia, D., and Torquati, M. (2019) New landscapes of the data stream processing in the era of fog computing. Futur. Gener. Comput. Syst., 99, 646–650. doi: 10.1016/j.future.2019.03.027.
110 110 Wei, X., Liu, Y., Wanga, X. et al. (2019) A survey on quality‐assurance approximate stream processing and applications. Futur. Gener. Comput. Syst., 101, 1062–1080.
111 111 Quoc, D.L., Krishnan, D.R., Bhatotia, P. et al. (2018) Incremental approximate computing, in Encyclopedia of Big Data Technologies (eds S. Sakr and A. Zomaya), Springer, Cham.
112 112 Sigurleifsson, B., Anbarasu, A., and Kangur, K. (2019) An overview of count‐min sketch and its application. EasyChair, 879, 1–7.
113 113 Garofalakis, M., Gehrke, J., and Rastogi, R. (eds) (2016) Data Stream Management: Processing High‐Speed Data Streams, Springer, Berlin, Heidelberg.
114 114 Sakr, S. (2016) Big Data 2.0 Processing Systems: A Survey, Springer, Switzerland. doi: 10.1007/978‐3‐319‐38776‐5.
115 115 Yates, J. (2020) Stream Processing with IoT Data: Challenges, Best Practices, and Techniques, https://www.confluent.io/blog/stream‐processing‐iot‐data‐best‐practices‐and‐techniques.
116 116