An~Adaptive Search Method for a~Logical Solving Function
Sibirskij žurnal industrialʹnoj matematiki, Tome 12 (2009) no. 3, pp. 66-74.

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

A method is proposed for seeking the optimal logical solving function in the problems of data analysis, which is a modification of adaptive random search. The method uses a metric in the space of logical solving functions and seeks solutions in accordance with an adaptable probability measure.
Keywords: pattern recognition, machine learning, solution trees, global extremum search, optimization, adaptive random search, genetic algorithms.
@article{SJIM_2009_12_3_a6,
     author = {G. S. Lbov and V. M. Nedel'ko and S. V. Nedel'ko},
     title = {An~Adaptive {Search} {Method} for {a~Logical} {Solving} {Function}},
     journal = {Sibirskij \v{z}urnal industrialʹnoj matematiki},
     pages = {66--74},
     publisher = {mathdoc},
     volume = {12},
     number = {3},
     year = {2009},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/SJIM_2009_12_3_a6/}
}
TY  - JOUR
AU  - G. S. Lbov
AU  - V. M. Nedel'ko
AU  - S. V. Nedel'ko
TI  - An~Adaptive Search Method for a~Logical Solving Function
JO  - Sibirskij žurnal industrialʹnoj matematiki
PY  - 2009
SP  - 66
EP  - 74
VL  - 12
IS  - 3
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/SJIM_2009_12_3_a6/
LA  - ru
ID  - SJIM_2009_12_3_a6
ER  - 
%0 Journal Article
%A G. S. Lbov
%A V. M. Nedel'ko
%A S. V. Nedel'ko
%T An~Adaptive Search Method for a~Logical Solving Function
%J Sibirskij žurnal industrialʹnoj matematiki
%D 2009
%P 66-74
%V 12
%N 3
%I mathdoc
%U http://geodesic.mathdoc.fr/item/SJIM_2009_12_3_a6/
%G ru
%F SJIM_2009_12_3_a6
G. S. Lbov; V. M. Nedel'ko; S. V. Nedel'ko. An~Adaptive Search Method for a~Logical Solving Function. Sibirskij žurnal industrialʹnoj matematiki, Tome 12 (2009) no. 3, pp. 66-74. http://geodesic.mathdoc.fr/item/SJIM_2009_12_3_a6/

[1] Hunt E. B., Marin J., Stone P. J., Experiments in Induction, Acad. Press, New York, 1966

[2] Bongard M. M., Problema uznavaniya, Nauka, M., 1967

[3] Zhuravlev Yu. I., “Ob algebraicheskom podkhode k resheniyu zadach raspoznavaniya ili klassifikatsii”, Problemy kibernetiki, 33, 1978, 5–68 | Zbl

[4] Vaintsvaig M. N., “Algoritm obucheniya raspoznavaniyu obrazov ‘KORA’ ”, Algoritmy obucheniya raspoznavaniyu obrazov, Sov. radio, M., 1973, 110–116

[5] Lbov G. S., Kotyukov V. I., Manokhin A. N., “Ob odnom algoritme raspoznavaniya v prostranstve raznotipnykh priznakov”, Vych. sistemy, 55, 1973, 97–107

[6] Lbov G. S., Metody obrabotki raznotipnykh eksperimentalnykh dannykh, Nauka, Novosibirsk, 1981 | Zbl

[7] Manokhin A. N., “Metody raspoznavaniya, osnovannye na logicheskikh reshayuschikh funktsiyakh”, Vych. sistemy, 67, 1976, 42–53 | Zbl

[8] Lbov G. S., Startseva N. G., Logicheskie reshayuschie funktsii i voprosy statisticheskoi ustoichivosti reshenii, Izd-vo In-ta matematiki, Novosibirsk, 1999 | MR | Zbl

[9] Quinlan J., “Induction of decision trees”, Mach. Learning, 1:1 (1986), 81–106

[10] Quinlan J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann Publ., San Francisco, 1993

[11] Breiman L., Friedman J. H., Olshen R. A., Stone C. J., Classification and Regression Trees, Wadsworth, Belmont, 1984 | MR | Zbl

[12] Berikov V. B., Lbov G. S., “Rekursivnye algoritmy resheniya zadach diskriminantnogo i regressionnogo analiza”, Mashinnoe modelirovanie i planirovanie eksperimenta, izd. NETI, Novosibirsk, 1991, 48–56

[13] Michalski R. S., “Pattern recognition as rule-guided inductive inference”, IEEE Trans. Pattern Anal. Mach. Intelligence, 2:4 (1980), 349–361 | DOI | Zbl

[14] Hyafil L., Rivest R. L., “Constructing optimal binary decision trees is NP-complete”, Inform. Process. Lett., 5:1 (1976), 15–17 | DOI | MR | Zbl

[15] Lbov G. S., “Vybor effektivnoi sistemy zavisimykh priznakov”, Vych. sistemy, 19, 1965, 21–34

[16] Kureichik V. M., “Geneticheskie algoritmy. Sostoyanie. Problemy. Perspektivy”, Izv. RAN. Teoriya i sistemy upravleniya, 1999, no. 1, 144–160 | MR | Zbl

[17] Papagelis A., Kalles D., “Breeding decision trees using evolutionary techniques”, Internat. Conf. Machine Learning, Morgan Kaufmann Publ., Williamstown, 2001, 393–400

[18] Kalles D., Papagelis A., Lossless fitness inheritance in genetic algorithms for decision trees, , 2006 arXiv: cs/0611166

[19] Kretowski M., Grzes M., “Global induction of oblique decision trees: an evolutionary approach”, Advances in Soft Computing, Springer-Verl., Berlin–Heidelberg, 2005, 309–318

[20] Donskoi V. I., “Kolmogorovskaya slozhnost klassov obscherekursivnykh funktsii s ogranichennoi emkostyu”, Tavrich. vestnik informatiki i matematiki, 2005, no. 1, 25–34 | Zbl

[21] Nedelko S. V., “Kriterii informativnosti matritsy perekhodov i prognozirovanie raznotipnogo vremennogo ryada”, Iskusstvennyi intellekt, 2004, no. 2, 145–149

[22] Nedelko V. M., “Ob intervalnom otsenivanii riska dlya reshayuschei funktsii”, Tavrich. vestnik informatiki i matematiki, 2008, no. 2, 97–103

[23] Sukharev A. G., Optimalnyi poisk ekstremuma, Izd-vo MGU, M., 1975

[24] Lopez de Mantaras R., “A distance-based attribute selection measure for decision tree induction”, Mach. Learning, 1991, no. 6, 81–92

[25] Simovici D. A., Jaroszewicz S., “A new metric splitting criterion for decision trees”, Parallel Algorithms Appl., 21:4 (2006), 239–256 | MR | Zbl

[26] Zagoruiko N. G., Prikladnye metody analiza dannykh i znanii, Izd-vo In-ta matematiki, Novosibirsk, 1999 | Zbl

[27] Kruskal J. B., “Multidimensional scaling by optimizing goodness of fit to a non-metric hypothesis”, Psychometrika, 29 (1964), 1–27 | DOI | MR