## 2016 (plans)

This paper is devoted to the problem of multiclass time series classification. It is proposed to align time series in relation to class centroids. Building of centroids and alignment of time series is carried out by the dynamic time warping algorithm. The accuracy of classification depends significantly on the metric used to compute distances between time series. The distance metric learning approach is used to improve classification accuracy. Themetric learning proceduremodifies distances between objects to make objects fromthe same cluster closer and from the different clusters more distant. The distance between time series is measured by the Mahalanobis metric. The distance metric learning procedure finds the optimal transformation matrix for the Mahalanobis metric. To calculate quality of classification, a computational experiment on synthetic data and real data of human activity recognition was carried out.

The paper provides a guidance on deep learning net construction and optimization using GPU. The paper proposes to use GPU-instances on the cloud platform Amazon Web Services. The problem of time series classification is considered. The paper proposes to use a deep learning net, i.e. a multilevel superposition of models, belonging to the following classes: Restricted Boltzman Machines, autoencoders and neural nets with softmax-function in output. The proposed method was tested on a dataset containing time segments from mobile phone accelerometer. The analysis of relation between classification error, dataset size and superposition parameter amount is conducted.

This paper discusses a problem of metric time series analysis and classification. The proposed classification model uses the matrix of distances between time series which is built with fixed distance function. The dimension of this distance matrix is very high and all related calculations are time-consuming. The problem of reducing the computational complexity is solved by selection reference objects and using them for describing classes. Model that uses dynamic time warping for building reference objects or centroids is chosen as a basic model. This paper introduces a function of weights for each centroid that influence on calculating the distance measure. Time series of different analytic functions and time series of human activity from accelerometer of mobile phone are used as the objects for classification. Properties and classification result of this model are investigated and compared with properties of basic model.

The paper presents analytic and stochastic methods of structure parameters estimation for model selection. Structure parameters are covariance matrices of parameters of linear and non-linear regression models. To optimize the model parameters and the structure parameters we maximize the model evidence including the data likelihood and the prior parameter distribution. The analytic methods are based on the approximated model evidence derivatives computation. The stochastic methods are based on the model parameters sampling and data cross-validation. The proposed methods are tested and compared on synthetic and real data.

The paper describes a univariate time series forecasting model. It proposes to find segments of local history, which are similar to the forecasted segment. A distance function is used to cluster segments. The forecast is the average of the value of time series from this cluster. To improve the quality of forecast the paper proposes an invariant transformation of segments. This transformation holds the equivalence of time series respect to clusters. The transformation is a function, constructed by the dynamic time warping procedure. The retrospective forecasting procedure calculates the accuracy of the forecasting model. Accelerometer time series of a person’s motion are used in computational experiment. It compares two constructing forecasting models. The first one clusters segments, the second one uses k-nearest neighbor algorithm to select similar segments.

We address a problem of increasing quality of forecasting time series by taking into account the information about exogenous factors. Our aim is to improve a special case of non-parametric forecasting algorithm, namely the hist algorithm, derived from quantile regression. The hist minimizes the convolution of a histogram of time series with the loss function. To include exogenous factors into this model we suggest to correct the histogram of endogenous time series, using exogenous time series. We propose to adjust the histogram, using mixtures of conditional histograms as a less sparse alternative to multidimensional histogram and in some cases demonstrate the decrease of loss compared to the basic forecasting algorithm. To the extent of our knowledge, such approach to combining endogenous and exogenous time series is original and has not been proposed yet. The suggested method is illustrated with the data from the Russian Railways.

This paper considers a problem of constructing a stable forecasting model using feature selection methods. It proposes a multicollinearity detection criterion, which is necessary in the case of excessive number of features. To investigate properties of this criterion, a theorem is stated. It develops the Belsley method. The proposed criterion runs an algorithm to exclude correlated features, reduce dimensionality of the feature space and to obtain robust estimations of the model parameters. The algorithm adds and removes features consequently according to this criterion. The LAD-Lasso algorithm was chosen as the basic to compare with. The computational experiment investigates an hourly-price forecasting curve problem with the proposed and the basic algorithms. The experiment carried out using time series of the German electricity prices.

Решается задача выбора оптимальной модели краткосрочного прогнозирования объемов железнодорожных перевозок по историческим и экзогенным временным рядам. Исторические данные содержат информацию об объемах перевозок различных типов грузов между парами станций. Предполагается, что результат выбора оптимальной модели зависит от уровня агрегирования по типам грузов, пунктам отправления и назначения и по времени. Рассмотрены модели векторной авторегрессии, интегрированная модель авторегрессионного скользящего среднего и непараметрическая модель гистограммного прогнозирования. Предложены критерии сравнения прогнозов на основе расстояний между ошибками прогнозов моделей. Данные критерии используются для анализа моделей с целью определения допустимых запросов на прогноз, в том числе, фактической глубины прогнозирования.

This paper solves the problem of selecting optimal stable models for classification of physical activity. We select optimal models from the class of two-layer artificial neural networks. There are three different ways to change structure of neurons: network pruning, network growing, and their combination. We construct models by removing its neurons. Neural networks with insufficient or excess number of neurons have insufficient generalization ability and can make unstable predictions. Proposed genetic algorithm optimizes the neural network structure. The novelty of the work lies in the fact that the probability of removing neurons is determined by the variance of parameters. In the computing experiment, models are generated by optimization two quality criteria — accuracy and stability.

При решении задач планирования в системах железнодорожного транспорта возникают проблемы связанные с нестационарностью, неравномерностью и высокой зашумленностью данных о грузоперевозках. Для повышения эффективности управления необходимо создание интеллектуальных систем, опирающихся на математические модели, исторические данные и формализованный опыт экспертов. Данная статья посвящена описанию проекта по созданию системы прогнозирования, направленной на повышение качества управления грузовыми железнодорожными перевозками путем выявления взаимосвязи объемов погрузки и спроса на грузовые железнодорожные перевозки с учетом экзогенных факторов.

## 2015

To detect small movements of Earth surface (with a velocity less than several centimeters per year) with use of SAR-interferometry methods it is necessary to find a number of surface areas remaining coherent on radar images over a long period. These areas and corresponding image points are called persistent scatterers. Two methods of persistent scatterers detection are consid-ered in the paper. The methods are compared by the number of detected points and their average time coherence. The algorithms considered are illustrated with an example of processing of a set containing 35 radar images.

The article is devoted to research of the algorithm of nonparametric forecasting of railway cargo transportation capacity. The problem considered is forecasting the number of wagons with various goods, following various routes. Topology of the railway network is given – for all possible pairs of railway lines information about all blocks of wagons, which have moved from one line to another, including the number of wagons in a block, type of cargo and date of a route, is provided. The algorithm, based on convolution of empirical density distribution of values ??of time series with loss function, is used for prediction. Previously forecast was carried out for each railway junction separately. Quality of the forecast is proposed to improve due to prediction by pairs of lines instead of predicting departure of all wagons from the given junction. The algorithm is illustrated by daily data on transportation of 38 types of cargo collected during year and a half.

This paper discusses a problem of time series classification in case of several classes. The proposed classification model uses the matrix of distance between time series. This distance measure is defined by dynamic time warping method. The dimension of the distance matrix is very high. This paper introduces centroids of each class as a reference objects to decrease this dimension. The distance matrix with lower dimension describes the distance between all objects and reference objects. We use this method for human activity recognition and investigate the quality of classification on data from the mobile accelerometer. This metric algorithm of classification is compared with separating classification algorithm.

The current generation of portable mobile devices incorporates various types of sensors that open up new areas for the analysis of human behavior. In this paper, we propose a method for human physical activity recognition using time series, collected from a single tri-axial accelerometer of a smartphone. Primarily, the method solves a problem of time series segmentation, assuming that each meaningful segment corresponds to one fundamental period of motion. To extract the fundamental period we construct the phase trajectory matrix, applying the technique of principal component analysis. The obtained segments refer to various types of human physical activity. To recognize these activities we use the k-nearest neighbor algorithm and neural network as an alternative. We verify the accuracy of the proposed algorithms by testing them on the WISDM dataset of labeled accelerometer time series from thirteen users. The results show that our method achieves high precision, ensuring nearly 96% recognition accuracy when using the bunch of segmentation and k-nearest neighbor algorithms.

This paper presents a new fast clustering algorithm RhoNet, based on the metric concenration location procedure. To locate the metric concentration, the algorithm uses a reduced matrix of pairwise ranks distances. The key feature of the proposed algorithm is that it doesn’t need the exhaustive matrix of pairwise distances. This feature reduces computational complexity. It is designed to solve the protein secondary structure recognition problem. The computational experiment collects tests and to hold performance analysis and analysis of dependency for the algorithm quality and structure parameters. The algorithm is compared with k-modes and tested on different metrics and data sets.

The paper investigates the multicollinearity problem in regression analysis and its influence on the performance of feature selection methods. The authors propose a procedure to test feature selection methods. A criteria is proposed to compare the feature selection methods, according to their performance when the multicollinearity is present. The feature selection methods are compared according to the other well-known evaluation measures. Methods to generate data sets of different multicollinearity types were proposed. The authors investigate performance of feature selection methods. The feature selection methods were tested on the data sets of different multicollinearity types.

This study investigates the multicollinearity problem and the performance of feature selection methods in case of datasets have multicollinear features. We propose a stresstest procedure for a set of feature selection methods. This procedure generates test data sets with various configurations of the target vector and features. A number of some multicollinear features are inserted in every configuration. A feature selection method results a set of selected features for given test data set. To compare given feature selection methods the procedure uses several quality measures. A criterion of the selected features redundancy is proposed. This criterion estimates number of multicollinear features among the selected ones. To detect multicollinearity it uses the eigensystem of the parameter covariance matrix. In computational experiments we consider the following illustrative methods: Lasso, ElasticNet, LARS, Ridge and Stepwise and determine the best one, which solve the multicollinearity problem for every considered configuration of dataset.

In this paper we investigate the problem of supervised latent modelling for extracting topic hierarchies from data. The supervised part is given in the form of expert information over document-topic correspondence. To exploit the expert information we use a regularization term that penalizes the dierence between a predicted and an expertgiven model. We hence add the regularization term to the log-likelihood function and use a stochastic EM based algorithm for parameter estimation. The proposed method is used to construct a topic hierarchy over the proceedings of the European Conference on Operational Research and helps to automatize the abstract submission system.

The paper addresses a problem of sensor-based time series segmentation as a part of human activity recognition problem. We assume that each studied time series contains a fundamenta periodic which can be seen as an ultimate entity (cycle) of motion. Due to the nature of the data and the urge to obtain interpretable results of segmentation, we defne the segmentation as a partition of the time series into the periods of this fundamental periodic. To split the time series into periods we select a pair of principal components of the Hankel matrix. We then cut the trajectory of the selected principal components by its symmetry axis, thus obtaining half-periods that are merged into segments. A method of selecting a pair of components, corresponding to the fundamental periodic is proposed.

In this paper we solve the problem of selecting optimal stable models for classification of physical activity. Each type of physical activity of a particular person is described by a set of features generated from the accelerometer time series. In conditions of feature’s multicollinearity selection of stable models is hampered by the need to evaluate a large number of parameters of these models. Evaluation of optimal parameter values is also difficult due to the fact that the error function has a large number of local minima in the parameter space. In the paper we choose the optimal models from the class of two-layer artificial neural networks. We solve the problem of finding the Pareto optimal front of the set of models. The paper presents a stepwise strategy of building optimal stable models. The strategy includes steps of deleting and adding parameters, criteria of pruning and growing the model and criteria of breaking the process of building. The computational experiment compares models generated by the proposed strategy on three quality criteria~— complexity, accuracy and stability.

This paper solves the problem of time-series classication using deep learning neural networks. The paper proposes to use a multilevel superposition of models belonging to the following classes of neural networks: two-layer neural networks, Boltzmann machines and autoencoders. Lower levels of superposition extract from noisy data of high dimensionality informative features, while the upper level of the superposition solves the problem of classication based on these extracted features. The proposed model has been tested on two samples of physical activity time series. The classication results obtained by proposed model in computational experiment were compared with the results which were obtained on the same datasets by foreign authors. The study showed the possibility of using deep learning neural networks for solving problems of time-series physical activity classication.

To determine movements of infrastructure objects on Earth surface, SAR interferometry is used. The method is based on obtaining a series of detailed satellite images of the same Earth surface area at different times. Each image consists of the amplitude and phase components. To determine terrain movements the change of the phase component is used. A method of persistent scatterers detection and estimation of relative shift of objects corresponding to persistent scatterers is suggested.

We solve an instance ranking problem using ordinal scaled expert estimations. The experts define a preference binary relation on the set of features. The instance ranking problem is considered as the monotone multiclass classification problem. To solve the problem we use a set of Pareto optimal fronts. The proposed method is illustrated with the problem of categorization of the IUCN Red List threatened species.

The hierarchical time series forecasting problem is researched. Time series forecasts must satisfy the physical constraints and the hierarchical structure. In this paper a new algorithm for hierarchical time series forecasts reconciliation is proposed. The algorithm is called GTOp (Game-theoretically Optimal reconciliation). It guarantees that reconciled forecasts quality is not worse than self-dependent forecasts one. This approach is based on Nash equilibrium search for the antagonistic game and turn forecasts reconciliation problem into the optimization problem with equality and inequality constraints. It is proved that the Nash equilibrium in pure strategies exists in the game if some assumptions about the hierarchical structure, the physical constraints and the loss function are satisfied. The algorithm performance is demonstrated for different types of hierarchical structures of time series.

This special issue on “Data Analysis and Intelligent Optimization with Applications” follows a previous special issue of this journal on the interplay of Machine Learning and Optimization, “Model Selection and Optimization in ML” (Machine Learning 85:1-2, October 2011). This time we shift our focus to applications of data analysis and optimization techniques. Optimization problems underlie most machine learning approaches. Due to emergence of new practical applications, new problems and challenges for traditional approaches arise. Emergent applications generate new data analysis problems, which, in turn boost new research in optimization. The contribution of machine learning researchers into the field of optimization is of considerable significance and should not be overlooked. This special issue collected solutions, adapted for real world problems, leading to massive and large-scale data sets, online data and imbalanced data. We encouraged submission of papers, devoted to combining machine learning and data analysis techniques with advances in optimization to produce methods of Intelligent Optimization, both theoretical and practical. Our goal for this special issue was to bring together researchers working in different areas, related to analytics and optimization.

## 2014

The article is dedicated to the problem of search engine results ranking. The algorithm of multiclass classifi cation with joint selection of features and objects is proposed. It is modifi ed for interclass relevance comparison. Features and objects selection is performed with stepwise regression and with genetic algorithm. Results obtained using both algorithms are compared. Proposed multiclass classifi cation algorithm is tested on synthetic data and on data of Yandex search engine results.

The aim of this paper is to verify a thematic structure of the conference abstracts collection. The conference consists of main Areas; each main Area consists of Streams; each Stream contains Sessions; Session consists of several talks. This conference structure determines a thematic model of the conference. Thousands of scientists submit their abstracts and participate in the a major conference, and the its thematic model of such conference has a multilevel structure. The program committee constructs an expert thematic model of the conference every year. Due to the huge number of experts in program committee, they meet the problem of thematic integrity verification occurs. The aim of this paper is to find inconsistences in the expert thematic model using the a text clustering approach. We consider an abstracts collection with an given expert model. The base assumption is that the terms of the abstract determine the theme of this abstract and its position location in the thematic model. We propose the a similarity function of two abstracts and . The introduce a quality function, which determines the quality of the thematic model. It considering involves the intracluster and intercluster similarities. The proposed fast non-metric clustering algorithm maximizes the this quality function. To make the some constructed model similar with the given expert model, the algorithm modity doesn’t change a the constructed model if the increase of the quality function exceeds is less than a some set fixed value of the threshold parameter value. This threshold impacts on the number of revealed inconsistences in the expert model. The proposed method constructs a thematic model for the abstracts for EURO 2013.

The paper presents new methods of alternatives ranking using expert estimations and measured data. The methods use expert estimations of objects quality and criteria weights. This expert estimations are changed during the computation. The expert estimation are supposed to be measured in linear and ordinal scales. Each object is described by the set of linear, ordinal or nominal criteria. The constructed object estimations must not contradict both the measured criteria and the expert estimations. The paper presents methods of expert estimations concordance. The expert can correct result of this concordance.

The problem of sample size estimation is important in the medical applications, especially in the cases of expensive measurements of immune biomarkers. The papers describes the problem of logistic regression analysis including model feature selection and includes the sample size determination algorithms, namely methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regression model parameters as the multivariate variable, propose to estimate sample size using the distance between parameter distribution functions on cross-validated data sets.

## 2013

Aduenko A., Strijov V. *Joint feature and object selection in multiclass classification of documents* // Infocommunication Technologies, 2013, 2 — ?. [Article]

Aduenko A., Strijov V. *Optimal text placement for titles of documents in collection* // Software Engineering, 2013, 3 — 21-25. [Article]

Budnikov E., Strijov V. *Estimating probabilities of text strings in document collections* // Information Technologies, 2013, 4 — 40-45. [Article]

Ivanova A., Aduenko A., Strijov V. *Algorithm of construction logical rules for text segmentation* // Software Engineering, 2013, 6 — 41-48. [Article]

Kuzmin A., Strijov V. *Validation of thematic models for document collections* // Software Engineering, 2013, 4 — 16-20. [Article]

Medvednikova M., Strijov V. *Construction of rank-scaled quality integral indicator for scientific publications in using co-clustering* // Notices of Tula State University, 2013, 1 — 154-165. [Article]

Rudoy G.I., Strijov V.V. *Algorithms for inductive generation of superpositions for approximation of experimental data* // Informatics and applications, 2013, 7(1) — 17-26. [Article]

Strijov V. *Error function in regression analysis* // Factory Laboratory, 2013, 79(5) — 65-73. [Article]

Strijov V., Krymova E., Weber G.W. *Evidence optimization for consequently generated models* // Mathematical and Computer Modelling, 2013, 57(1-2) — 50-56. [Article]

Tsyganova S., Strijov V. *The construction of hierarchical thematic models for document collection* // Applied Informatics, 2013, 1 — 109-115. [Article]

Varfolomeeva A., Strijov V. *Feature selection for bibliographic records marking with methods of structure learning* // Notices on Science and Technology of S.-Pb.PSU, 2013, 2 — ?. [Article]

Zaytsev A., Strijov V., Tokmakova A. *Estimation regression model hyperparameters using maximum likelihood* // Informational Technologies, 2013, 2 — 11-15. [Article]

Aduenko A.A., Kuzmin A.A., Strijov V.V. *Hierarchical thematic model visualizing algorithm* // 26th European Conference on Operational Research, 2013 — 155. [Inproceedings]

Kuznetsov M.P., Strijov V.V. *The IUCN Red List threatened speices categorization algorithm* // 26th European Conference on Operational Research, 2013 — 352. [Inproceedings]

Strijov V.V. *Credit Scorecard Development: Model Generation and Multimodel Selection* // 26th European Conference on Operational Research, 2013 — 220. [Inproceedings]

## 2012

Aduenko A., Kuzmin A., Strijov V. *Feature selection and metrics optimisation for document collection clustering* // Notices of Tula State University, 2012, 3 — 119-131. [Article]

Kuznetsov M., Strijov V. *Fast clustering algorithm for the objects, described by the rank-scaled distance matrix* // Mathematical biology and bioinfomatics-?, 2012, ? — ?.

Kuznetsov M., Strijov V., Medvednikova M. *Multiclass classification of objects with the rank-scale description* // Notices on Science and Technology of SPb. PSU, 2012, 5 — 92-95. [Article]

Medvednikova M., Strijov V., Kuznetsov M. *Algorithm of multiclass monotonous Pareto-classification* // Notices of Tula State University, 2012, 3 — 132-141. [Article]

Motrenko A., Strijov V.V. *Multiclass logistic regression for cardio-vascular disease forecasting* // Notices of Tula State University, 2012, 1 — 153-162. [Article]

Sanduleanu L., Strijov V. *Feature selection for autoregressive forecasting* // Informational Technologies, 2012, 6 — 11-15. [Article]

Strijov V., Kuznetsov M., Rudakov K. *Rank-scaled metric clustering of amino-acid sequences* // Mathematical Biology and Bioinformatics, 2012, 7(1) — 345-359. [Article]

Tokmakova A., Strijov V. *Estimation of linear model hyperparameters for noisy or correlated feature selection problem* // Informatics and applications, 2012, 6(4) — 66-75. [Article]

Kuznetsov M., Strijov V. *Integral indicator construction using rank-scaled design matrix* // Intellectual Information Processing. Conference proceedings, 2012 — 130-132. [Inproceedings]

Rudoy G.I., Strijov V.V. *Simplification of superpositions of primitive functions with graph rule-rewriting* // Intellectual Information Processing. Conference proceedings, 2012 — 140-143. [Inproceedings]

Strijov V. *Sequental model selection in forecasting* // 25th European Conference on Operational Research, 2012 — 176. [Inproceedings]

Tokmakova A., Strijov V. *Estimation of linear model hyperparametres for noise or correlated feature selection problem* // Intellectual Information Processing. Conference proceedings, 2012 — 156-159. [Inproceedings]

## 2011

Krymova E., Strijov V. *Feature selection algorithms for linear regression models from finite and countable sets* // Factory laboratory, 2011, 77(5) — 63-68. [Article]

Strijov V. *Specification of rank-scaled expert estimation using measured data* // Factory laboratory, 2011, 77(7) — 72-78. [Article]

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. [Article]

Strijov V., Krymova E. *Model selection in linear regression analysis* // Informational Technologies, 2011, 10 — 21-26. [Article]

Kuznetsov M., Strijov V. *Integral Indicators and Expert estimations of Ecological Impact* // International Conference on Operations Research, 2011 — 32. [Inproceedings]

Kuznetsov M., Strijov V. *Monotonic interpolation for the rank-scaled expert estimations specification* // Proceedings of Mathematical Methods of Pattern Recognition. МАКС~Пресс, 2011 — 162-165. [Inproceedings]

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. [Inproceedings]

Strijov V. *Multilevel model selection using parameters covariance matrix analysis* // Proceedings of Mathematical Methods of Pattern Recognition. МАКС~Пресс, 2011 — 154-157. [Inproceedings]

Strijov V. *Invariants and model selection in forecasting* // International Conference on Operations Research, 2011 — 133. [Inproceedings]

## 2010

Strijov V., Weber G.W. *Nonlinear regression model generation using hyperparameter optimization* // Computers and Mathematics with Applications, 2010, 60(4) — 981-988. [Article]

Strijov V. *Methods of regression model selection*. Moscow, Computing Center RAS, 2010 — 60. [Book]

Krymova E., Strijov V. *Model selection and multicollinearity analysis* // Proceedings of conference on Intelligent data processing, 2010 — 153-156. [Inproceedings]

Skipor K., Strijov V. *Least angle logistic regression* // Proceedings of conference on Intelligent data processing, 2010 — 180-183. [Inproceedings]

Strijov V. *Evidence of successively generated models* // International Conference on Operations Research “Mastering Complexity”, 2010 — 223. [Inproceedings]

Strijov V. *Model generation and model selection in credit scoring* // 24th European Conference on Operations Research, 2010 — 220. [Inproceedings]

Strijov V., Krymova K., Weber G.W. *Evidence Optimization for Consequently Generated Models* // Proceedings of the fourth global conference on power control and optimization, 2010, 1337 — 204-208.

Strijov V., Letmathe P. *Integral indicators based on data and rank-scale expert estimations* // Intellectual Data Analysis: the International Scientific Conference Proceedings, 2010 — 107-110. [Inproceedings]

Strijov V., Weber G.W., Dolgopolova I. *Model Generation and Mathematical Modelling* // EngOpt 2010: 2nd International Conference on Engineering Optimization, 2010 — 169. [Inproceedings]

## 2009

Strijov V., Sologub R. *The inductive generation of the volatility smile models* // Journal of Computational Technologies, 2009, 14(5) — 102-113. [Article]

Krymova E., Strijov V. *Comparison of the heuristic algorithms for linear regression model selection* // Mathematical methods for pattern recognition. Conference proceedings. MAKS Press, 2009 — 145-148. [Inproceedings]

Melnikov D., Strijov V., Anderrva E., Edenharter G. *Selection of support object set for robust integral indicator construction* // // Mathematical methods for pattern recognition. Conference proceedings. MAKS Press, 2009 — 159-162. [Inproceedings]

Strijov A., Strijov V. *Specification of the rank-scaled expert estimations* // Mathematics. Computer. Education. Conference Proceedings, 2009 — 41. [Inproceedings]

Strijov V. *Model selection using inductively generated set* // European Conference on Operational Research EURO-23, 2009 — 114. [Inproceedings]

Strijov V. *Model generation and model selection* // Mathematics. Computer. Education. Conference Proceedings, 2009. [Inproceedings]

Strijov V. *The Inductive Algorithms of Model Generation* // SIAM Conference on Computational Science and Engineering, 2009. [Inproceedings]

Strijov V., Granic G.and Juric Z., Jelavic B., Maricic S. *Integral Indicator of Ecological Footprint for Croatian Power Plants* // HED Energy Forum “Quo Vadis Energija in Times of Climate Change”, 2009 — 46. [Inproceedings]

Strijov V., Krymova E. *Algorithms of linear model generation* // Mathematics. Computer. Education. Conference Proceedings, 2009. [Inproceedings]

Strijov V., Sologub R. *Generation of the implied volatility models* // Mathematics. Computer. Education. Conference Proceedings, 2009. [Inproceedings]

Strijov V., Sologub R. *Algorithm of nonlinear regression model selection by analysis of hyperparameters* // Mathematical methods for pattern recognition. Conference proceedings. MAKS~Press, 2009 — 184-187. [Inproceedings]

## 2008

Strijov V. *The methods for the inductive generation of regression models*. Moscow, Computing Center RAS, 2008. [Book]

Bray D., Strijov V. *Using immune markers for classification of the CVD patients* // Intellectual Data Analysis: Abstracts of the International Scientific Conference, 2008 — 49-50. [Inproceedings]

Gushchin A., Strijov V. *An algorithm on the expert estimations objectification with measured data* // Intellectual Data Analysis: the International Scientific Conference, 2008 — 78-79. [Inproceedings]

Sologub R.and Strijov V. *The inductive construction of the volatility regression models* // Intellectual Data Analysis: the International Scientific Conference Proceedings, 2008 — 215-216. [Inproceedings]

Strijov V. *On the inductive model generation* // Intellectual Data Analysis: Abstracts of the International Scientific Conference, 2008 — 220. [Inproceedings]

Strijov V. *Clusterization of multidimensional time-series using dynamic time warping* // Mathematics. Computer. Education. Conference Proceedings, 2008 — 28. [Inproceedings]

Strijov V. *Estimation of hyperparameters on parametric regression model generation* // 9th International Conference on Pattern Recognition and Image Analysis: New Information Technologies, 2008, 2 — 178-181. [Inproceedings]

Strijov V., Sologub R. *The inductive generation of the volatility smile models* // SIAM Conference on Financial Mathematics and Engineering 2008, 2008 — 21. [Inproceedings]

Vorontsov K., Inyakin A., Lisitsa A., Strijov V., Khachay M., Chekhovich Y. *Proof-ground for classification algorithms: the distributed computing system* // Intellectual Data Analysis: the International Scientific Conference, 2008 — 54-56.

Vorontsov K., Inyakin A., Strijov V., Chekhovich Y. *MachineLearning.ru~— a site, devoted to problems of pattern recognition, forecasting and classification* // Intellectual Data Analysis: the International Scientific Conference, 2008 — 56-58.

## 2007

Strijov V. *The search for a parametric regression model in an inductive-generated set* // Journal of Computational Technologies, 2007, 1 — 93-102. [Article]

Strijov V. *Parametric regression model selection in the inductively-generated set* // Computational Technologies, 2007, 1 — 93-102.

Strijov V., Kazakova T. *Stable indices and the choice of a support description set* // Zavodskaya Laboratoriya, 2007, 7 — 72-76. [Article]

Strijov V., Ptashko G. *Algorithms of the optimal regression model selection*. Computing Center of the Russian Academy of Sciences, 2007 — 56. [Book]

Ivakhnenko A., Kanevskiy D., Rudeva A., Strijov V. *How to compare marked time-series* // Proc. Mathematical Methods of Pattern Recognition, 2007 — 134-137. [Inproceedings]

Strijov V., Kazakova T. *The rank-scaled expert estimations concordance* // Proc. Mathematical Methods of Pattern Recognition, 2007 — 209-211. [Inproceedings]

Strijov V., Ptashko G. *The invariants of time series and dynamic time warping* // Proc. Mathematical Methods of Pattern Recognition, 2007 — 212-214. [Inproceedings]

## 2006

Kazakova T., Strijov V. *The robust indicators with normalising functions selection* // Artificial intelligence, 2006, 1 — 160-163. [Article]

Strijov V. *The search for regression models in an inductive-generated set* // Artificial intelligence, 2006, 2 — 234-237. [Article]

Strijov V. *Specification of expert estimations using measured data* // Factory Laboratory, 2006, 72(7) — 59-64. [Article]

Strijov V. *Vsevolod Vladimirovich Shakin* // Mathematics. Computer. Education. Conference Proceedings. Regular and chaotic dynamics, 2006, 1 — 5-16. [Incollection]

Kazakova T., Strijov V. *The robust indicators with normalising functions selection* // International Scientific Conference on Artificial Intelligence, 2006 — 199. [Inproceedings]

Strijov V. *Indices construction using linear and ordinal expert estimations* // Citizens and Governance for Sustainable Development, 2006 — 49. [Inproceedings]

Strijov V. *The search for regression models in a set of smooth functions* // Mathematics. Computer. Education. Conference Proceedings, 2006. [Inproceedings]

Strijov V. *The search for regression models in an inductive-generated set* // International Scientific Conference on Artificial Intelligence, 2006 — 198. [Inproceedings]

Strijov V., Kazakova T. *Robust indicators and selection of support objects* // Multivariate statistical analysis applications in economics and quality assessment. VIII-th International Conference, 2006. [Inproceedings]

## 2005

Strijov V. *On the isomorphic automata synthesis problem* // Natural and Technical Sciences, 2005, 4 — 61-65. [Article]

Strijov V. *Mathematical modelling on the Natural Protected Area management* // Actual Problems of Modern Science, 2005, 5 — 79-84. [Article]

Kazakova T., Strijov V. *Stable integral indices* // Proc. Mathematical Methods of Pattern Recognition, 2005 — 206. [Inproceedings]

Ptashko G., Strijov V. *The distance function choice for the phase trajectories comparison* // Proc. Mathematical Methods of Pattern Recognition, 2005 — 116-119. [Inproceedings]

Ptashko G., Strijov V., Shakin V. *Specification of ordinal expert estimations* // Mathematics. Computer. Education. Conference Proceedings, 2005. [Inproceedings]

Strijov V. *How to select a nonlinear regression model of optimal complexity?* // Proc. Mathematical Methods of Pattern Recognition, 2005 — 190-191. [Inproceedings]

Strijov V., Shakin V. *Selection of optimal regression model* // Mathematics. Computer. Education. Conference Proceedings, 2005. [Inproceedings]

## 2003

Strijov V., Shakin V. *Index construction: the expert-statistical method* // Environmental research, engineering and management, 2003, 26(4) — 51-55. [Article]

Strijov V., Shakin V. *Forecast and control with autoregressive models* // Proc. Mathematical Methods of Pattern Recognition conference, 2003 — 178-181. [Inproceedings]

Strijov V., Shakin V. *Index construction: the expert-statistical method* // Proc. Conference on Sustainability Indicators and Intelligent Decisions, 2003 — 56-57. [Inproceedings]

Aivazian S., Strijov V., Shakin V. *On a problem of macroeconomics management*. Computing Center of the Russian Academy of Sciences, 2003. [Techreport]

## 2002

Strijov V. *Expert estimations concordance for biosystems under extreme conditions. Notes on applied mathematics*. Moscow, Coumpiting Center of RAS, 2002. [Book]

Molak V., Strijov V., Shakin V. *Kyoto-Index for power plants in the USA* // Mathematics. Computer. Education. Conference Proceedings, 2002 — 292. [Inproceedings]

Strijov V., Shakin V. *Rank-scaled expert estimations concordance* // International Scientific Conference on Artificial Intelligence, 2002 — 82-83. [Inproceedings]

Strijov V., Shakin V. *Rank-scaled expert estimations processing* // Mathematics. Computer. Education. Conference Proceedings, 2002 — 148. [Inproceedings]

Strijov V. *Specification of expert estimations for integral indicators construction (thesis abstract)*. Computing Center of the Russian Academy of Sciences, 2002 — 24. [Phdthesis]

Strijov V. *Specification of expert estimations for integral indicators construction (thesis manuscript)*. Computing Center of the Russian Academy of Sciences, 2002 — 105. [Phdthesis]

Strijov V. *Time management for development of electronic devices*, 2002 — 2. [Techreport]

Strijov V., et al. *Methodology elements of the university research effectiveness estimations. Part~1.*, 2002 — 7. [Techreport]

Strijov V., et al. *Methodology elements of the university research effectiveness estimations. Part~2.*, 2002 — 7. [Techreport]

Strijov V., Shakin V. *Ordering of mixed-scaled objects*, 2002 — 8. [Techreport]

## 2001

Karioukhin E., Shakin V., Strijov V., Matunin E., Izgacheva T., Kazakova T. *Mathematical modelling of gerontology-support organizations* // The Clinical Gerontology. Scientific Journal. Moscow: Newdiamed, 2001, 7(8) — 89.

Strijov V. *Bidirectional CBT chips application* // Schemotechnics, 2001, 2 — 18-19. [Article]

Strijov V. *CMOS buffers* // Schemotechnics, 2001, 2 — 20-21. [Article]

Strijov V. *Live plug-in* // Schemotechnics, 2001, 5 — 15-18. [Article]

Strijov V., Shakin V. *An algorithm for clustering of the phase trajectory of a dynamic system* // Mathematical Communications, Supplement, 2001, 1 — 159-165. [Article]

Matunin E., Izgacheva T., Kazakova T., Karioukhin E., Strijov V., Shakin V. *Mathematical modelling and informational support for gerontology organizations*. Computing Center of the Russian Academy of Sciences, 2001 — 79.

Molak V., Shakin V., Strijov V. *Kyoto Index for power plants in the USA* // The 3-rd Moscow International Conference On Operations Research, 2001 — 80.

Strijov V., Shakin V. *Expert estimations concordance* // Proc. Mathematical Methods of Pattern Recognition, 2001 — 137-138. [Inproceedings]

Strijov V., Shakin V., Blagovidov K. *Concordance of expert estimations for analysis of protected areas management effectiveness* // Multivariate statistics analysis applications in economics and quality estimation, 2001 — 30. [Inproceedings]

Zubarevich H., Tikunov B., Krepets V., Strijov V., Shakin V. *Multivariate methods for human development index estimation in Russian regions* // GIS for area sustainable development. International Conference Proceedings, 2001 — 84-105.

Strijov V., Shakin V. *Analysis of a dynamic system phase trajectory*, 2001 — 5. [Techreport]

Strijov V., Shakin V., Blagovidov K. *Analysis of protected areas management effectiveness*, 2001 — 11. [Techreport]

Strijov V., Shakin V., Blagovidov K. *A model of the Protected Areas Management*, 2001 — 7. [Techreport]

## 2000

Strijov V. *Square pulse generators with CMOS ICs* // Schemotechnics, 2000, 3 — 25-26. [Article]

Strijov V. *The IC behavior under low-voltage* // Schemotechnics, 2000, 2 — 32-33.

Strijov V. *The simplest PCI interface* // Schemotechnics, 2000, 1 — 55-57. [Article]

Strijov V. *Logic IC with 3V power supply* // Schemotechnics, 2000, 3 — 14-15. [Article]

Strijov V., Shakin V. *An algorithm for clustering of the phase trajectory of a dynamic system* // 8-th International Conference on Operational Research, KOI-2000, 2000 — 35. [Inproceedings]

## 1999

Strijov V. *Phase trajectory analysis software and its applications* // Problems of the complex system safety control. VII-th International Conference Proceedings, 1999 — 156-157. [Inproceedings]

Strijov V., Shakin V. *Phase trajectory analysis software* // Proc. Mathematical Methods of Pattern Recognition, 1999 — 227-230. [Inproceedings]

## 1997

Strijov V. *Motorola IC for TV, video and multimedia overview*. Moscow: Motorola GmbH, 1997 — 75. [Book]

## 1996

Strijov V. *Configurable processors for biomedical data visualizing* // Biosystems under extreme conditions. Computing Center of the Russian Academy of Sciences, 1996 — 47-50. [Incollection]