Диссертация (Исследование и разработка методов и алгоритмов обобщения знаний для систем поддержки принятия решений реального времени), страница 22
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2. — Pp. 188–228.Kin-pong Chan, Ada Wai-Chee Fu. Efficient Time Series Matching byWavelets // ICDE. — 1999. — Pp. 126–133.Christos Faloutsos, M. Ranganathan, Yannis Manolopoulos. Fast SubsequenceMatching in Time-Series Databases // SIGMOD Conference. — 1994. —Pp. 419–429.A Symbolic Representation of Time Series, with Implications for StreamingAlgorithms / Jessica Lin, Eamonn Keogh, Stefano Lonardi, Bill Chiu // InProceedings of the 8th ACM SIGMOD Workshop on Research Issues in DataMining and Knowledge Discovery.
— 2003. — Pp. 2–11.Experiencing SAX: a novel symbolic representation of time series / Jessica Lin,Eamonn Keogh, Li Wei, Stefano Lonardi // Data Min. Knowl. Discov. — 2007.— oct. — Vol. 15, no. 2. — Pp. 107–144. — URL: http://dx.doi.org/10.1007/s10618-007-0064-z.Shieh Jin, Keogh Eamonn. iSAX: indexing and mining terabyte sized timeseries // Proceedings of the 14th ACM SIGKDD international conference onKnowledge discovery and data mining. — KDD ’08.
— New York, NY, USA:ACM, 2008. — Pp. 623–631. — URL: http://doi.acm.org/10.1145/1401890.1401966.Keogh Eamonn, Lin Jessica, Fu Ada. HOT SAX: Efficiently Finding the MostUnusual Time Series Subsequence // Proceedings of the Fifth IEEE InternationalConference on Data Mining. — ICDM ’05. — Washington, DC, USA: IEEEComputer Society, 2005. — Pp.
226–233. — URL: http://dx.doi.org/10.1109/ICDM.2005.79.Time-series bitmaps: a practical visualization tool for working with large timeseries databases / Nitin Kumar, Nishanth Lolla, Eamonn Keogh et al. // SIAM2005 Data Mining Conference. — SIAM, 2005. — Pp. 531–535.Keogh, E., Zhu, Q., Hu, B. et al. — 2011. — URL: www.cs.ucr.edu/~eamonn/time_series_data.13264. Chen, Yanping, Keogh, Eamonn, Hu, Bing et al.
The UCR Time Series Classification Archive. — 2015. — July. — www.cs.ucr.edu/~eamonn/time_series_data/.65. Lichman M. UCI Machine Learning Repository. — 2013. — URL: http://archive.ics.uci.edu/ml.66. Varun Chandola, Arindam Banerjee, Vipin Kumar. Anomaly Detection - A Survey // ACM Computing Surveys. — 2009. — Vol. 41(3). — Pp. 1–72.67. Anderson James P.
Computer security threat monitoring and surveillance: Tech.Rep. : James P. Anderson Co., Fort Washington, Pa., 1980.68. Larose Daniel T. Discovering Knowledge in Data: An Introduction to Data Mining. — Wiley-Interscience, 2004. — 222 pp.69. Shyam Boriah, Varun Chandola, Vipin Kumar. Similarity measures for categorical data: A comparative evaluation // In Proceedings of the eighth SIAMInternational Conference on Data Mining. — 2008. — Pp.
243–254.70. Jain Anil K., Dubes Richard C. Algorithms for clustering data. — Upper SaddleRiver, NJ, USA: Prentice-Hall, Inc., 1988.71. А. Н. Колмогоров. Три подхода к определению понятия «количество информации» // Проблемы передачи информации. — 1965. — Т. 1. — С. 3–11. —URL: http://mi.mathnet.ru/ppi68.72. Shannon C. E.
A mathematical theory of communication // Bell System TechnicalJournal. — 1948. — Vol. 27. — Pp. 379–423, 623–656.73. Anomaly detection in transportation corridors using manifold embedding. /Agovic, A., Banerjee, A., Ganguly, A. R., Protopopescu, V. // First International Workshop on Knowledge Discovery from Sensor Data. — ACM Press,2007.74. C. Stefano, C. Sansone, M. Vento.
To reject or not to reject: that is the question - an answer in case of neural classifiers // IEEE Transactions on Systems,Management and Cybernetics. — 2000. — Vol. 1. — Pp. 84–94.75. Outlier Detection Using Replicator Neural Networks / Simon Hawkins, Hongxing He, Graham Williams, Rohan Baxter // In Proc.
of the Fifth Int. Conf.and Data Warehousing and Knowledge Discovery (DaWaK02. — 2002. —Pp. 170–180.76. Barbara, D., Wu, N., Jajodia, S. Detecting Novel Network Intrusions usingBayes Estimators // Proc. SIAM Intl. Conf. Data Mining. — 2001.13377. Sebyala, A. A., Olukemi, T., Sacks, L. Active platform security through intrusiondetection using naive bayesian network for anomaly detection // In Proceedingsof the 2002 London Communications Symposium. — 2002.78. Constructing Boosting Algorithms from SVMs: An Application to One-ClassClassification / Rätsch, Gunnar, Mika, Sebastian, Schölkopf, Bernhard,Müller, Klaus-Robert // IEEE Trans.
Pattern Anal. Mach. Intell. — 2002. —sep. — Vol. 24, no. 9. — Pp. 1184–1199. — URL: http://dx.doi.org/10.1109/TPAMI.2002.1033211.79. Using artificial anomalies to detect unknown and known network intrusions /Fan, W., Miller, M., Stolfo, S. et al. // Knowl. Inf. Syst. — 2004. — sep. — Vol. 6,no. 5. — Pp. 507–527. — URL: http://dx.doi.org/10.1007/s10115-003-0132-7.80.
Agrawal, Rakesh, Srikant, Ramakrishnan. Mining Sequential Patterns // Proceedings of the Eleventh International Conference on Data Engineering. — ICDE’95. — Washington, DC, USA: IEEE Computer Society, 1995. — Pp. 3–14. —URL: http://dl.acm.org/citation.cfm?id=645480.655281.81. Saito Naoki. Local feature extraction and its application using a library of bases:Ph.D. thesis / Yale University.
— 1994.82. Kadous Mohammed Waleed. Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series: Ph.D. thesis / University of NewSouth Wales. — New South Wales, Australia, Australia, 2002. — AAI0806481.83. Company Western Electric. Statistical quality control handbook.
— New York,USA: Mack Printing Company, Easton, Pennsylvania, 1958.84. D. T. Pham, A. B. Chan. Control Chart Pattern Recognition using a New Type ofSelf Organizing Neural Network // Proceedings of the Institution of MechanicalEngineers, Part I: Journal of Systems and Control Engineering. — 1998. — Vol.212(2). — Pp. 115–127.85. Hui-Ping Cheng, Chuen-Sheng Cheng. Control Chart Pattern Recognition UsingWavelet Analysis and Neural Networks // Journal of Quality. — 2009. — Vol. 16.— Pp. 311–321.86. Robert T.
Olszewski. Generalized Feature Extraction for Structural PatternRecognition in Time-Series Data: Ph.D. thesis / School of Computer Science,Carnegie Mellon University, Pittsburgh. — 2001.87. Transformation Based Ensembles for Time Series Classification / A. Bagnall,L. Davis, J. Hills, J.
Lines // Proceedings of the 12th SIAM International13488.89.90.91.92.93.94.95.96.Conference on Data Mining (SDM 2012). — 2012. — Pp. 307–319.Roverso Davide. Multivariate Temporal Classification By Windowed WaveletDecomposition And Recurrent Neural Networks // In 3 rd ANS InternationalTopical Meeting on Nuclear Plant Instrumentation, Control and Human-MachineInterface. — 2000.Roverso Davide. Neural and Fuzzy Transient Classification Systems: GeneralTechniques and Applications in Nuclear Power Plants // Fuzzy Systems and SoftComputing in Nuclear Engineering / Ed.
by Da Ruan. — Physica-Verlag HD,2000. — Vol. 38 of Studies in Fuzziness and Soft Computing. — Pp. 208–234. —URL: http://dx.doi.org/10.1007/978-3-7908-1866-6_10.Genetic Algorithms and Support Vector Machines for Time Series Classification / Eads, Damian, Hill, Daniel, Davis, Sean et al. // Proc.
SPIE 4787; FifthConference on the Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation; Signal Processing Section; Annual Meeting of SPIE. — 2002. — URL: http://www.zeus.lanl.gov/green/publications/eadsSPIE4787.pdf.URL: http://code.google.com/p/lbimproved.Estimating the Support of a High-Dimensional Distribution / Schölkopf, Bernhard, Platt, John C., Shawe-Taylor, John C.
et al. // Neural Comput. — 2001.— jul. — Vol. 13, no. 7. — Pp. 1443–1471. — URL: http://dx.doi.org/10.1162/089976601750264965.Roth Volker. Kernel Fisher Discriminants for Outlier Detection // Neural Comput. — 2006. — apr. — Vol. 18, no. 4. — Pp. 942–960. — URL: http://dx.doi.org/10.1162/089976606775774679.Andreas Arning, Rakesh Agrawal, Prabhakar Raghavan. A Linear Method forDeviation Detection in Large Databases // In Proceedings of KDD’1996. — 1996.— Pp. 164–169.Eamonn Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana. Towards parameter-free data mining // Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining.
— KDD ’04.— New York, NY, USA: ACM, 2004. — Pp. 206–215. — URL: http://doi.acm.org/10.1145/1014052.1014077.Quinlan J. R. Improved Use of Continuous Attributes in C4.5 // Machine learning. — 1996. — Vol. 4. — Pp. 77–90.13597. Utgoff Paul E. Incremental Induction of Decision Trees // Machine learning. —1989. — Vol. 4. — Pp. 161–186.98.
Большая советская энциклопедия. Т. 25. Струнино - Тихорецк. / Под ред.А. М. Прохоров. — Издание второе, исправленное и дополненное изд. —Москва, 1976. — 600 с.99. Селлерс Ф. Методы обнаружения ошибок в работе ЭЦВМ, пер. с англ.,. —Москва, 1972.100. Основы технической диагностики / В. В. Карибский, П. П. Пархоменко,Е. С. Согомонян, В. Ф.
Халчев. — Москва: Энергия, 1976.101. A Spectrum of Definitions for Temporal Model-Based Diagnosis / Vittorio Brusoni, Luca Console, Paolo Terenziani, Daniele Theseider Dupre // Artificial Intelligence. — 1998. — Vol. 102. — Pp. 39–79.102. Luca Console, Oskar Dressler. Model-based Diagnosis in the Real World:Lessons Learned and Challenges Remaining // Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. — IJCAI ’99. — San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1999. — Pp. 1393–1400. —URL: http://dl.acm.org/citation.cfm?id=646307.688062.103.
Оськин П.В. Исследование и реализация систем поддержки истинности длязадач диагностики: Ph.D. thesis / Московский энергетический институт. —2007.104. Production system models of learning and development / Ed. by David Klahr,P. Langley, R. Neches. — Cambridge, MA: MIT Press, 1987.105. Zadeh L.A. Fuzzy Sets // Information Control.
