Abstract
Practical precedent-based recognition algorithms relying on logical or algebraic correction of various heuristic recognition algorithms are described. The recognition problem is solved in two stages. First, an arbitrary object is recognized independently by algorithms from a group. Then a final collective solution is produced by a suitable corrector. The general concepts of the algebraic approach are presented, practical algorithms for logical and algebraic correction are described, and results of their comparison are given.
Similar content being viewed by others
References
R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973; Mir, Moscow, 1976).
A. N. Dmitriev, Yu. I. Zhuravlev, and F. P. Krendelev, “Mathematical principles of classification of patterns and scenes,” in Discrete Analysis (Inst. Mat. Sib. Otd. Akad. Nauk SSSR, Novosibirsk, 1966), Vol. 7, pp. 3–11.
L. V. Baskakova and Yu. I. Zhuravlev, “A model of recognition algorithms with representative samples and systems of supporting sets,” USSR Comput. Math. Math. Phys. 21(5), 189–199 (1981).
Yu. I. Zhuravlev and V. V. Nikiforov, “Recognition algorithms based on calculated estimates,” Kibernetika, No. 3, 1–11 (1971).
V. V. Ryazanov, “On the optimization of a class of recognition models,” Pattern Recogn. Image Anal. 1(1), 108–118 (1991).
P. Wasserman, Neurocomputing: Theory and Practice (Van Nostrand Reinhold, New York, 1990; Mir, Moscow, 1992).
C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning 20(3), 273–297 (1995).
Yu. I. Zhuravlev, “On the algebraic approach to recognition and classification problems,” in Problems in Cybernetics (Nauka, Moscow, 1978), Vol. 33, pp. 5–68 [in Russian].
Yu. I. Zhuravlev, “Correct algebras over sets of incorrect (heuristic) algorithms, Parts I-III,” Kibernetika, No. 4, 5–17 (1977); No. 6, 21–27 (1977); No. 2, 35–43 (1978).
Yu. A. Zuev, “A method for improving classification reliability when several classifiers are available, based on the principle of monotonicity,” USSR Comput. Math. Math. Phys. 21(1), 156–166 (1981).
A. M. Veshtort, Yu. A. Zuev, and V. V. Krasnoproshin, “Two-level recognition system with a logical correct,” in Recognition, Classification, and Prediction: Mathematical Methods and Applications (Nauka, Moscow, 1989), Vol. 2, pp. 73–98 [in Russian].
L. A. Aslanyan, L. F. Mingo, J. B. Castellanos, F. B. Chelnokov, A. A. Dokukin, V. V. Ryazanov, “On logical correction of neural network algorithms for pattern recognition,” Proceedings of the 4th International Conference on Information Research and Applications (Foi-commerce, Sofia, 2006).
A. G. D’yakonov, “Algebra over estimation algorithms: The minimal degree of correct algorithms,” Comput. Math. Math. Phys. 45(6), 1095–1106 (2005).
A. G. D’yakonov, “Algebra over estimation algorithms: Normalization with respect to the interval,” Comput. Math. Math. Phys. 49(1), 194–202 (2009).
L. Bottou, C. Cortes, J. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. Le Cun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: A case study in handwritten digit recognition,” Proceedings of the 12th IAPR International Conference on Pattern Recognition (IEEE Computer Soc., Jerusalem, Israel, 1994), pp. 77–87.
Yu. I. Zhuravlev, V. V. Ryazanov, and O. V. Sen’ko, Pattern Recognition: Mathematical Methods, Software System, and Applications (Fazis, Moscow, 2006) [in Russian].
Yu. I. Zhuravlev, S. V. Ablameyko, A. S. Biryukov, A. A. Dokukin, V. V. Krasnoproshin, V. V. Obraztsov, M. Yu. Romanov, V. V. Ryazanov, “Algebraic and logical correction algorithms and their applications,” Pattern Recogn. Image Anal. 20(2), 105–117 (2010).
Yu. I. Zhuravlev and I. V. Isaev, “Construction of recognition algorithms correct for a given control sample,” USSR Comput. Math. Math. Phys. 19(3), 175–189 (1979).
A. A. Dokukin, “A Method for constructing an optimal estimate calculation algorithm,” Comput. Math. Math. Phys. 46(4), 719–725 (2006).
A. A. Dokukin, “On the construction of samples for testing approximate optimization methods for estimate calculation algorithms,” Comput. Math. Math. Phys. 46(5), 914–918 (2006).
M. Yu. Romanov, “A method for constructing a recognition algorithm in algebra over an estimate calculation set,” Comput. Math. Math. Phys. 47(8), 1368–1372 (2007).
M. Yu. Romanov, “Implementation of a method for constructing a recognition algorithm in algebra over an estimate calculation set,” Comput. Math. Math. Phys. 48(9), 1680–1686 (2008).
A. M. Veshtort, S. I. Kashkevich, S. B. Kostyukevich, V. V. Krasnoproshin, and S. G. Sinyakovich, “Principles of constructing an automated system of aerospace spectrometry data for physiographic zoning,” Izv. Akad. Nauk SSSR Ser. Geogr., No. 1, 89–94 (1988).
V. S. Glushenkov, A. A. Kovalev, O. L. Konovalov, S. B. Kostyukevich, V. V. Krasnoproshin, and B. A. Yukhimenko, “Computer-based system of mapping and editing of radiation and ecological digital maps,” Vestsi ANB, Ser. Fiz.-Mat. Navuk 5-6, 102–107 (1992).
S. V. Gafurov and V. V. Krasnoproshin, “System-constructing program technique for solving recognition problems with a complex structure,” Iskusstv. Intellekt, No. 1, 30–37 (2008).
V. V. Ryazanov, “Logical regularities in pattern recognition (parametric approach),” Comput. Math. Math. Phys. 47(10), 1720–1735 (2007).
V. I. Donskoi and A. I. Bashta, Discrete Models of Decision Making with Incomplete Data (Tavriya, Simferopol’, 1992).
P. Horton and K. Nakai, “A Probabilistic classification system for predicting the cellular localization sites of proteins,” Intelligent Syst. Molecular Biol. St. Louis, USA, 109–115 (1996).
O. L. Mangasarian and W. H. Wolberg, “Cancer diagnosis via linear programming,” SIAM News 23(5), 1–18 (1990).
D. Harrison and D. L. Rubinfeld, “Hedonic prices and the demand for clean air,” J. Environ. Econ. Management 5, 81–102 (1978).
V. G. Sigillito, S. P. Wing, L. V. Hutton, and K. B. Baker, “Classification of radar returns from the ionosphere using neural networks,” Johns Hopkins APL Tech. Digest 10, 262–266 (1989).
H. Ganster, M. Gelautz, A. Pinz, M. Binder, H. Pehamberger, M. Bammer, and J. Krocza, “Initial results of automated melanoma recognition,” Proceedings of the 9th Scandinavian Conference on Image Analysis (Uppsala, Sweden, 1995), Vol. 1, pp. 209–218.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © S.V. Ablameyko, A.S. Biryukov, A.A. Dokukin, A.G. D’yakonov, Yu.I. Zhuravlev, V.V. Krasnoproshin, V.A. Obraztsov, M.Yu. Romanov, V.V. Ryazanov, 2014, published in Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, 2014, Vol. 54, No. 12, pp. 1979–1993.
Rights and permissions
About this article
Cite this article
Ablameyko, S.V., Biryukov, A.S., Dokukin, A.A. et al. Practical algorithms for algebraic and logical correction in precedent-based recognition problems. Comput. Math. and Math. Phys. 54, 1915–1928 (2014). https://doi.org/10.1134/S0965542514120033
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0965542514120033