Combinatorial analysis of the solvability properties of the problems of recognition and completeness of algorithmic models. Part 1: Factorization approach
Tóm tắt
Từ khóa
Tài liệu tham khảo
Yu. I. Zhuravlev, “Correct algebras for sets of incorrect (heuristic) algorithms. Part I,” Kibernetika, No. 4, 5–17 (1977).
Yu. I. Zhuravlev, “Correct algebras for sets of incorrect (heuristic) algorithms. Part II,” Kibernetika, No. 6, 21–27 (1977).
Yu. I. Zhuravlev, “Correct algebras for sets of incorrect (heuristic) algorithms. Part III,” Kibernetika, No. 2, 35–43. (1978).
Yu. I. Zhuravlev, “On algebraic approach for solving recognition and classification problems,” in Problems of Cybernetics (Nauka, Moscow, 1978), Issue 33, pp. 5–68 [in Russian].
K. V. Rudakov, “On some universal limitations for classification algorithms,” Zh. Vychisl. Mat. Mat. Fiz. 26 (11), 1719–1729 (1986).
K. V. Rudakov, “Universal and local limitations in the problem on heuristic algorithms correction,” Kibernetika, No. 2, 30–35 (1987).
K. V. Rudakov, “Completeness and universal limitations in the problem on heuristic classification algorithms correction,” Kibernetika, No. 3, 106–109 (1987).
K. V. Rudakov, “The way to apply universal limitations for researching classification algorithms,” Kibernetika, No. 1, 1–5 (1988).
K. V. Rudakov, On Algebraic Theory of Universal and Local Limitations for Classification Problems. Recognition, Classification, Prediction (Nauka, Moscow, 1989), pp. 176–201 [in Russian].
I. Yu. Torshin and K. V. Rudakov, “On the theoretical basis of metric analysis of poorly formalized problems of recognition and classification,” Pattern Recogn. Image Anal. 25 (4), 577–579 (2015).
I. Y. Torshin and K. V. Rudakov, “On metric spaces arising during formalization of recognition and classification problems. Part 1: properties of compactness,” Pattern Recogn. Image Anal. 26 (2), 274–284 (2016).
I. Y. Torshin and K. V. Rudakov, “On metric spaces arising during formalization of problems of recognition and classification. Part 2: density properties,” Pattern Recogn. Image Anal. 26, 483 (2016).
K. V. Rudakov, “The theory of universal and local limitations for recognition algorithms,” Doctoral Dissertation in Mathematical Physics (Moscow, Dorodnicyn Computing Centre RAS, 1992).
K. V. Rudakov and I. Yu. Torshin, “The way to analyze motifs’ information capability according to solvability criteria in the problem on protein secondary structure recognition,” Inf. Ee Prim. 6 (1), 79–90 (2012).
Yu. I. Zhuravlev, K. V. Rudakov, and I. Yu. Torshin, “Algebraic criteria of local solvability and regularity as a tool for researching morphology of amino acid regularities,” Trudy Mosk. Fiz.-Tekhn. Inst. 3 (4), 45–54 (2011).
I. Yu. Torshin, “The study of the solvability of the genome annotation problem on sets of elementary motifs,” Pattern Recogn. Image Anal. 21 (4), 652–662 (2011).
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer-Verlag, 2009).
K. V. Vorontsov, “Combinatory theory of precedent learning reliability,” Doctoral Dissertation in Mathematical Physics (Moscow, Dorodnicyn Computing Centre RAS, 2010).
S. V. Yablonskii, Introduction into Discrete Mathematics (Nauka, Moscow, 1986) [in Russian].
A. N. Kolmogorov, Selected Works. Probability Theory and Mathematical Statistics (Moscow, 1986) [in Russian].
N. V. Smirnov, “The way to approximate random varieties distribution laws according to empirical data,” Usp. Mat. Nauk, No. 10, 179–206 (1944).
L. N. Bol’shev and N. V. Smirnov, Tables of Mathematical Statistics (Nauka, Moscow, 1983) [in Russian].