Source: http://strijov.com/?p=6
Timestamp: 2019-04-19 09:17:38+00:00

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Please note that papers in English are marked «En», otherwise they are «Ru».
Вычислительные технологии, 2013, ? &#151 ?.
Kuznetsov M., Strijov V. Fast clustering algorithm for the objects, described by the rank-scaled distance matrix // Mathematical biology and bioinfomatics-?, 2012, ? — ?.
Motrenko A., Strijov V. Multiclass logistic regression for cardio-vascular disease forecasting // Izvestiya TulSU, Natural sciences, 2012, 1 — ?.
Sanduleanu L., Strijov V. Feature selection for autoregressive forecasting // Informational technologies, 2012, 6 — ?.
Tokmakova A., Strijov V. Estimation of hyperparameters for noise and coreccated feature selection problem // Informatics and applications, 2012, 4 — ?.
Krymova E., Strijov V. Feature selection algorithms for linear regression models from finite and countable sets // Factory laboratory, 2011, 77(5) — 63-68.
Strijov V., Krymova E. Model selection in linear regression analysis // Informational technologies, 2011, 10 — 21-26.
Kuznetsov M., Strijov V. Monotonic interpolation for the rank-scaled espert estomations specification // Proceedings of Mathematical Methods of Pattern Recognition. МАКС~Пресс, 2011 — 162-165.
Kuznetsov M., Strijov V. Integral Indicators and Expert estimations of Ecological Impact // International Conference on Operations Research, 2011 — 32.
Pavlov K., Strijov V. Multilevel model selection in the bank credit scoring applications // Proceedings of Mathematical Methods of Pattern Recognition. МАКС~Пресс, 2011 — 158-161.
Strijov V., Granic G., Juric J., Jelavic B., Maricic S.A. Integral indicator of ecological impact of the Croatian thermal power plants // Energy, 2011, 36(7) — 4144-4149.
Strijov V., Krymova E., Weber G.W. Evidence optimization for consequently generated models // Mathematical and Computer Modelling, 2011, 2(17) — 10.1016/j.mcm.2011.02.017.
Strijov V. Multilevel problem selection using parameters covariance matrix analysis // Proceedings of Mathematical Methods of Pattern Recognition. МАКС~Пресс, 2011 — 154-157.
Strijov V. Specification of rank-scaled expert estimation using measured data // Factory laboratory, 2011, 77(7) — 72-78.
Strijov V. Invariants and model selection in forecasting // International Conference on Operations Research, 2011 — 133.
Skipor, K., Strijov, V. Least angle logistic regression // Intelligent Data Analysis, 2011.
Strijov, V., Granic, G., Juric, J., Jelavic, B., Maricic, S.A. // Integral Indicator of Ecological Footprint for Croatian Power Plants // Energy, 2011.
Strijov, V.V., Krymova, E.A., Weber, G.-W. Evidence Optimization for Consequently Generated Models // Mathematical and Computer Modelling, 2011.
Strijov, V.V., Krymova, E.A. Feature selection algorithms for inductively generated linear regression models // Factory laboratory, 2011.
Krymova, E.A., Strijov, V.V. Model selection in regression analysis // Informational technologies, 2011.
Strijov V. Model Selection Using Inductively Generated Set / 23rd European Conference on Operational Research. Abstracts. Bonn, July 5-8, 2009. P. 114.
Strizhov A., Strijov V. Specification of rank-scaled expert estimations using measured data / 23rd European Conference on Operational Research. Abstracts. Bonn, July 5-8, 2009. P. 209.
The procedure of the search for a parametric regression model in a model set is described. The model set is a set of superpositions of given smooth functions. The model parameters density estimates are used for the search. To illustrate the approach a problem of modelling a pressure in a spray chamber of a combustion engine is included.
This paper describes an integral indicator construction algorithm. The integral indicator is a linear combination of the object features. The features are in the linear scale. Outliers in the features are assumed. So the problem of stable integral indicators construction arises. To construct a stable integral indicator a special-defined subset of objects is selected. A non-supervised type of algorithm is used to make the integral indicator. The proposed algorithm was applied to construct the integral indicator of the foodstuff pollution level in the Russian regions.
The optimal regression model search procedure is described. The model is defined by a superposition of a smooth functions. Probability density functions of model parameters are used. The parameters are estimated with non-linear optimization methods. A problem of diesel engine pressure modelling presents an application of the method.
The problem of stable integral indicators for an object set is considered. The expert estimates of the objects are used. The indicators are computed as a linear combination of the object features and corrected with the expert estimates. Well known algorithms are involved to compare the proposed method with.
There a sample set of dependent variables and one independent variable are given. There a set of nongenerated functions, which define a set of regression models is given. The paper describes an algorithm of optimal regression model choice. The algorithm uses hyperparameters to estimate model elements importance.
Kazakova, T., Strijov, V. The robust indicators with normalising functions selection // Artificial intelligence. 2006. No 2. P. 160-163. [strijov06AIidx.pdf, Ru].
The problem of stable integral indicators for an object set is considered. The objects are featured in the linear scales. To construct a stable integral indicator one has to choose an objects features subset such that causes the maximal value to the stable criterion.
A feedback model of control the Protected areas is described. The model involves the control subject and object. The subject set the goals of the control and according to the goals chooses one of the several variants of control. The object state monitoring is holding during the process of control. The described model involves Protected area annual reports and expert estimates.
The paper deals with the index construction and presents a new technique that involves expert estimations of object indices as well as feature significance weights. The method based on a linear indexing model for a set of objects index is calculated as a linear combination of the object feature descriptions. Well-known methods of index construction with “no teacher” are overviewed to give a comparison with the new method. Experts can take part in the index calculation and verify the results, which are: the first, precise valid indices and the second, we have the reasoned expert estimations. The methods with or without expert involvement were used for solution of listed below different economical, sociological, and ecological problems.
To construct an index one has to collect data on objects. After data acquisition he has a table “object-feature”. Using the table he can make the required index. For that one chooses one of mathematical methods such as Singular Components, Principle Components, Pareto Slicing and the others. The mentioned methods also called as “non-expert” methods.
Experts, that have their own opinions in particular applications, could to assign expert estimations to the objects. Expert estimations can prove or disprove the indices that were results of “non-expert” methods. The proposed technique solve problem of concordance the expert estimations and indices.
The expert estimations concordance technique is considered. The specified expert-estimations were applied to the project on the State Protected Areas effectiveness evaluation. In the paper the theory of effectiveness indicators is described together with the application project results and the software library.
Matunin, E., Izgacheva, T., Kazakova, T., Karioukhin, E., Strijov, V., Shakin, V. Mathematical modelling and informational support on gerontology organizations. Moscow: CCAS. 2001. 79 p.
Molak, V, Shakin, V. Strijov, V. Kyoto Index for the Power Plants in the USA // The 3-rd Moscow International Conference On Operations Research. Abstracts. Moscow: CCAS. 2001. P. 80.
Karioukhin, E., Shakin, V., Strijov, V., Matunin, E., Izgacheva, T., Kazakova, T. Mathematical modelling on gerontology support organizations // The Clinical Gerontology. Scientific journal. Moscow: Newdiamed. Vol. 7-8. 2001. P. 89.
This paper describes an approach to quantitative analysis of multivariate dynamic system in phase space. The system is used as mathematical model for various living systems. One of the related problems is to represent a phase trajectory as a sequence of clusters to classify the system’s state. The algorithm for partitioning a phase trajectory into clusters is presented. Input data for the algorithm is a data matrix, which corresponds to a set of sequential samples of the given phase trajectory. Optional parameters are dimension of the space and phase trajectory noise variance. The algorithm results is a tree-like graph. Phase trajectory of a dynamic system with Lorenz attractor is considered as a test problem to demonstrate the approach. The given phase trajectory was partitioned into clusters using the described algorithm.
Zubarevich, H, Tikunov, B., Krepets, V.,Strijov, V., Shakin, V. Multivariate methods for human development index estimation in Russia Federation regions // Proc. GIS for area sustainable development. Petropavlovsk-Kamchatskii. 2001. P. 84-105.
Strijov, V. The IC behavior under lack of the voltage. Moscow: Schemotekhnica. No 5. 2000. P. 32-33.

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