Laboartory of Data Analysis

Mottl V.V., Muchnik I.B. Linguistic analysis of experimental curves. Proceedings IEEE, 1979, Vol. 67, № 5, pp. 714-736.

Degtyarev N., Seredin O.: A Geometric Approach to Face Detector Combining // In C. Sansone, J. Kittler, and F. Roli (Eds.): MCS 2011, LNCS 6713, pp. 299–308, Springer-Verlag Berlin Heidelberg (2011). Available at: http://lda.tsu.tula.ru/papers/DegtyarevMCS2011PID29.pdf The original publication is Available at www.springerlink.com

Degtyarev N., Seredin O.: Comparative Testing of Face Detection Algorithms: In A. E lmoataz et al. (Eds.): ICISP 2010, LNCS 6134, pp. 200--209, Springer, Heidelberg (2010). Available at: http://lda.tsu.tula.ru/papers/degtyarev-2010-icisp-ctfd.pdf The original publication is Available at www.springerlink.com

Degtyarev N., Seredin O.: Effect of Eyes Detection and Position Estimation Methods on the Accuracy of Comparative Testing of Face Detection Algorithms // 10th International Conference on Pattern Recognition and Image Analysis: New Information Technologies (PRIA-10-2010). St. Petersburg, December 5-12, 2010. Conference Proceedings, Volume 2, Politechnika, 2010, pp. 261-264 . Available at: http://lda.tsu.tula.ru/papers/degtyarev_FDLC_stp2010.pdf

Dvoyenko S.D., Mottl V.V., Muchnik I.B. Pattern recognition in experimental waveforms. Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications. 1991. Vol. 1. No. 1, pp. 87-98.

Ivanova T.O., Mottl V.V., Muchnik I.B. Estimation of parameters of hidden Markov models for noise-like signals with changing probabilistic properties. I,II. Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications, 1993, Vol. 3, № 2, pp. 99-112, 113-125.

Krasotkina O.V., Mottl V.V., Kopylov A.V., Algorithms of Estimation of Nonstatio-nary Regression in Signal Analysis, In: Pattern Recognition and Image Analysis, Vol. 13, No. 1, 2003, pp. 127-131. Available at: Krasotkina_kra-kop-mottl-e.pdf

Krasotkina, O., Mottl, V. Adaptive nonstationary regression analysis, In: 19th International Conference on Pattern Recognition, 2008 , pp. 1--4, IEEE (2009). Available at: http://lda.tsu.tula.ru/papers/Krasotkina_icpr_krasotkina_3.pdf The original publication is Available at ieeexplore.ieee.org DIO: 10.1109/ICPR.2008.4761666

Krasotkina O., Kopylov A., Mottl V., M. Markov Bayesian Estimation of Time-Varying Regression with Changing Time-Volatility for Detection of Hidden Events in Non-Stationary Signals, In Proceedings of the 7th IASTED International Conference, Vol.678, Numb.050 pp.50 Innsbruck, Austria (2010). Available at: http://lda.tsu.tula.ru/papers/Krasotkina_SIP-2010-9-krasotkina.pdf The original publication is Available at www.actapress.com

Krasotkina, O. PAIR-WISE SEPARABLE QUADRATIC PROGRAMMING FOR CONSTRAINED TIME-VARYING REGRESSION ESTIMATION, In Proceedings of the 7th IASTED International Conference, Vol.678, Numb.070 pp.70 Innsbruck, Austria (2010). Available at: http://lda.tsu.tula.ru/papers/Krasotkina_678-070.pdf The original publication is Available at www.actapress.com

Markov, M., Muchnik, I., Mottl, V., Krasotkina, O.: Dynamic analysis of hedge funds, In Proceedings: 3rd IASTED International Conference on Financial Engineering and Applications, Track#546-028, ACTA Press, Cambridge, (2006). Available at: http://lda.tsu.tula.ru/papers/Krasotkina_dsa-camera-final-RFBR.pdf The original publication is Available at www.actapress.com

Markov, M. and Muchnik, I., Mottl, V., Krasotkina O. Machine-Learning for Dynamic Reverse Engineering of Hedge Funds, In: International Conference on Machine Learning and Cybernetics 2007, vol. 5, pp. 2805--2812, IEEE (2007). Available at: http://lda.tsu.tula.ru/papers/Krasotkina_MLfDRE.pdf The original publication is Available at ieeexplore.ieee.org

Mottl V.V., Kopylov A.V., Blinov A.B., Zheltov S.Yu. Quasi-statistical approach to the problem of stereo image matching. SPIE Proceedings, 1994, Vol. 2363, pp. 50- 61. Mottl V.V., Dvoyenko S.D. Supervised recognition of events in signals with changing probabilistic properties. 1995 IEEE Workshop on Nonlinear Signal and Image Processing. Neos Marmaras, Greece, June 20-22. Vol. 1, pp. 238-241.

Mottl V.V., Blinov A.B. A texture processing algorithm and its application to seismic section segmentation. The 4th Open Russian German Workshop “Pattern Recognition and Image Analysis”. Valday, Russian Federation, March 3-9, 1996, pp. 103-106.

Mottl V.V., Blinov A.B., Kopylov A.V. Generalized technique for a class of image analysis problems based on tree-like quasi-Markov models of the hidden information. The 4th Open Russian German Workshop “Pattern Recognition and Image Analysis”. Valday, Russian Federation, March 3-9, 1996, pp. 107-111.

Mottl V.V., Muchnik I.B., Blinov A.B., Kopylov A.V. Hidden tree-like quasi-Markov model and generalized technique for a class of image processing problems. 13th International Conference on Pattern Recognition. Vienna, Austria, August 25-29, 1996. Track B, pp. 715-719.

Mottl, V. Dvoenko, S. Seredin, O. Kulikowski, C. Muchnik, I.: Featureless Pattern Recognition in an Imaginary Hilbert Space and Its Application to Protein Fold Classification, In: P.Perner (Ed.): MLDM 2001, LNAI 2123, pp. 322-336, Springer-Verlag, Berlin, Heidelberg (2001). Available at: http://lda.tsu.tula.ru/papers/Seredin_2001.pdf The original publication is Available at www.springerlink.com

Mottl V.V., Seredin O.S., Dvoenko S.D., Kulikowski C.A, Muchnik I.B. Featureless pattern recognition in an imaginary Hilbert space. In: Proceedings of 16th International Conference Pattern Recognition, ICPR-2002, Quebec City, Canada, August, 2002, vol.II, pp.88-91. Available at: http://lda.tsu.tula.ru/papers/Seredin_2002.pdf The original publication is Available at ieeexplore.ieee.org

Mottl V.V., Kopylov A.V., Krasotkina O.V. A Quadratic Programming Procedure for Elastic Image Matching, In: Proceeding of Automation, Control, and Information Technology 2002 (ACIT 2002), Track# 372-032 Novosibirsk, Russia (2002). Available at: http://lda.tsu.tula.ru/papers/Krasotkina_QPMatch.pdf The original publication is Available at www.actapress.com

Mottl, V., Krasotkina, O., Muchnik, I. Constrained nonstationary signal processing by pair-wise separable quadratic programming, In:IASTED International Conference on Signal Processing, Pattern Recognition, and Applications pp. 205-208, Rhodes,Greece (2003). Available at: http://lda.tsu.tula.ru/papers/Krasotkina_404-083.pdf

Mottl V.V., Seredin O.S., Krasotkina O.V., Muchnik I.B. Fusion of Euclidian metrics in featureless data analysis: an equivalent of the classical problem of feature selection In: Proceedings of 7th International Conference on Pattern Recognition and Image Analysis, PRIA-7-2004, St. Petersburg, October, 2004, pp.94-97. Available at: http://lda.tsu.tula.ru/papers/Seredin_2004.pdf

Mottl V.V., Seredin O.S., Krasotkina O.V., and Muchnik I.B. Kernel fusion and feature selection in machine learning. Proceedings of the eighth IASTED International Conference Intelligent Systems and Control, October 31 – November 2, 2005, Cambridge, USA, pp.477-482. Available at: http://lda.tsu.tula.ru/papers/Krasotkina_497-077.pdff The original publication is Available at www.actapress.com

Mottl V.V., Seredin O.S., Krasotkina O.V., and Muchnik I.B.: Principles of multi-kernel data mining. In: P. Perner and A. Imiya (Eds.), Machine Learning and Data Mining in Pattern Recognition, Springer Verlag, LNAI 3587, 2005, pp. 52 – 61. Available at: http://lda.tsu.tula.ru/papers/Seredin_2005.pdf The original publication is Available at www.springerlink.com

Mottl V., O. Seredin O., Sulimova V. Mathematically correct methods of similarity measurement on sets of signals and symbol sequences of different length. Pattern Recognition and Image Analysis,Vol.15, No.1, 2005, pp.87-89. Available at: http://lda.tsu.tula.ru/papers/sulimova-2005-PRIA-Mathematically_Correct_Methods.pdf

Mottl V., Sulimova V., Tatarchuk A. Multi-kernel approach to on-line signature verification. In: Proceedings of the Eighth IASTED International Conference on Signal and Image Processing, held August 14 – 16, 2006, Honolulu, Hawaii, USA, pp. 448-453. Available at: http://lda.tsu.tula.ru/papers/sulimova-2006-IASTED-Multy-Kernel_Approach.pdf The original publication is Available at www.actapress.com

Mottl V., Tatarchuk A., Sulimova V., Krasotkina O., SeredinO. Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion, In: Proceedings of 7th International Workshop Multiple Classifiers Systems, Prague, Czech Republic, pp. 1–12, LNCS 4472, Springer-Verlag, Berlin, Heidelberg (2007). Available at: http://lda.tsu.tula.ru/papers/sulimova-2007-MCS-Combining_pattern_recognition_modalities.pdf The original publication is Available at www.springerlink.com

Mottl V., Lange M., Sulimova V., Yermakov A. Signature verification based on fusion of on-line and off-line kernels. In: Proc. of 19-th International conference on Pattern Recognition (ICPR 2008), Florida, USA, December 2008. pp. 1-4. Available at: http://lda.tsu.tula.ru/papers/sulimova-2008-ICPR-Signature_Verification_Based_on_Fusion.pdf The original publication is Available at ieeexplore.ieee.org

Mottl V., Krasotkina O., Ezhova E. Estimation of Nonstationary Linear Regression with Unknown Time-Variability via Continuous Generalization of the Akaike Information Criterion, Technical Report, Tula State University, Laboratory of Data Analysis, Tula, Russia (2010). Available at: http://lda.tsu.tula.ru/papers/Krasotkina_Akaike-Buenos-Aires.pdf

Muchnik I.B., Mottl V.V. Bellman Functions on Trees for Segmentation, Generalized Smoothing, Matching and Multi-Alignment in Massive Data Sets. DIMACS Technical Report 98-15, February 1998. Center for Discrete Mathematics and Theoretical Computer Science. Rutgers University, the State University of New Jersey, USA. Available at: ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/1998/98-15.ps.gz

Mottl V.V., Kostin A.A., Muchnik I.B. Generalized Edge-Preserving Smoothing for Signal Analysis. International Workshop on Nonlinear Signal and Image Analysis. Mackinac Island, Michigan, USA, September 7-11, 1997.

Mottl V.V., Blinov A.B., Kopylov A.V., Kostin A.A. Optimization techniques on pixel neighborhood graphs for image processing. Graph Based Representations in Pattern Recognition. Computing, Supplement 12. Springer, 1998. Pp. 135-145.

Seredin O.S. Experimental Study of A Priori Preferences for Decision Rules in Hilbert Spaces of Recognition Objects. Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications, 2003, Vol. 13, No. 1, pp. 55–58. Available at: http://lda.tsu.tula.ru/papers/Seredin_2003.pdf

Seredin O., Mottl V. Regularization in Image Recognition: the Principle of Decision Rule Smoothing In: Proceedings of the Ninth International Conference Pattern Recognition and Information Processing, Minsk, Belarus, 2007. Vol.II., pp. 151-155. Available at: http://lda.tsu.tula.ru/papers/Seredin_2007.pdf

Sulimova V., Mottl V., Muchnik, I. Kernel functions for signals and symbolic sequences of different length. In: Proc. of The International Conference on Pattern Recognition and Image Analysis: New Information technologies. Yoshkar-Ola, The Russian Federation, October 2007, pp.155-158. Available at: http://lda.tsu.tula.ru/papers/sulimova-2007-ROAI-Kernel_Functions_for_signals_and_sequences.pdf

Sulimova V., Mottl V., Mirkin B., Muchnik I., Kulikowski C. A Class of Evolution-Based Kernels for Protein Homology Analysis: A Generalization of the PAM Model. In: Proc. of 5th International Symposium on Bioinformatics Research and Applications, Nova Southeastern University, Ft. Lauderdale, Florida, USA, May 13-16, 2009, LNBI 5542, pp. 284--296, Springer-Verlag, Berlin, Heidelberg (2009). Available at: http://lda.tsu.tula.ru/papers/sulimova-2009-ISBRA-A_Class_of_Evoluion-based_kernels.pdf The original publication is Available at www.springerlink.com

Tatarchuk A., Sulimova V., Windridge D., Mottl V., M. Lange.: Supervised Selective Combining Pattern Recognition Modalities and Its Application to Signature Verification by Fusing On-Line and Off-Line Kernels. In: Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Volume 5519/2009, pp. 324-334, 2009. Available at: http://lda.tsu.tula.ru/papers/sulimova-2009-MCS-Supervised_Selective_Combining.pdf The original publication is Available at www.springerlink.com