Geodesic
Method of automated construction of pattern recognition algorithms on phase paths
Modelirovanie i analiz informacionnyh sistem, Tome 16 (2009) no. 4, pp. 6-21.

Voir la notice de l'article provenant de la source Math-Net.Ru

The problem of automated construction of recognizers of anomalies in the behavior of complicated dynamical systems is solved by means of analyzing trajectories obtained from sensors surrounding the system. A specific feature of the problem consists in the fact that, depending on the individual properties of the system and conditions of its operation, trajectories that contain anomalies may significantly differ from each other in amplitude and length. Besides, the training set could be incompletely defined. The algorithm described here is based on the idea of applying an algebraic approach to the labeling of trajectories. It allows to construct recognizers of abnormal behavior of complicated dynamical systems. The training of the algorithm could be done on an incompletely defined training set.
Keywords: machine learning, recognition algorithm, training set, problem of constructing an algorithm on a training set, algebraic approach. 
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D. S. Kovalenko. Method of automated construction of pattern recognition algorithms on phase paths. Modelirovanie i analiz informacionnyh sistem, Tome 16 (2009) no. 4, pp. 6-21. http://geodesic.mathdoc.fr/item/MAIS_2009_16_4_a1/

[1] K. V. Rudakov, Yu. V. Chekhovich, “O probleme sinteza obuchayuschikh algoritmov vydeleniya trendov (algebraicheskii podkhod)”, Prikladnaya matematika i informatika, 8, Izd-vo fakulteta VMiK MGU, M., 2001, 97–114

[2] D. S. Kovalenko, V. A. Kostenko, E. A. Vasin, “Issledovanie primenimosti algebraicheskogo podkhoda k analizu vremennykh ryadov”, Metody i sredstva obrabotki informatsii, Izd-vo fakulteta VMiK MGU, M., 2005, 553–559

[3] K. V. Vorontsov, “Kombinatornyi podkhod k otsenke kachestva obuchaemykh algoritmov”, Matematicheskie voprosy kibernetiki, 13, 2004, 5–36 http://www.ccas.ru/frc/papers/voron04mpc.pdf | MR | Zbl

[4] D. S. Kovalenko, V. A. Kostenko, “Metod postroeniya algoritmov raspoznavaniya, osnovannykh na ideyakh aksiomaticheskogo podkhoda”, Sbornik nauchnykh trudov XI Vserossiiskoi nauchno-tekhnicheskoi konferentsii «Neiroinformatika-2009», Izd-vo MIFI, M., 2009

[5] Eamonn J. Keogh, Michael J. Pazzani, “Derivative Dynamic Time Warping”, First SIAM International Conference on Data Mining (SDM'2001) (Chicago, USA), 2001 http://www.ics.uci.edu/~pazzani/Publications/sdm01.pdf

[6] D. S. Kovalenko, “Metody nechetkogo sravneniya i golosovaniya dlya postroeniya raspoznavatelei neshtatnogo povedeniya dinamicheskikh sistem”, Trudy V Moskovskoi mezhdunarodnoi konferentsii po issledovaniyu operatsii «ORM-2007», MAKS Press, M., 2007, 123–125

[7] D. L. Danilov, A. A. Zhiglyavskii, Glavnye komponenty vremennykh ryadov: metod «Gusenitsa», Sankt-Peterburgskii universitet, SPb., 1997