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检索条件"机构=Department of Data Analysis and Machine Learning"

共 160 条记录，以下是31-40 订阅

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Set-valued prediction in multi-class classification 31

Set-valued prediction in multi-class classification

引用

31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on machine learning, BNAIC/BENELEARN 2019

作者： Mortier, Thomas Wydmuch, Marek Dembczýnski, Krzysztof Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modelling Ghent University Belgium Institute of Computing Science Poznán University of Technology Poland Intelligent Systems and Machine Learning Universität Paderborn Germany

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with highest expected utility. © 2019 for this paper by its authors.

关键词： Forecasting

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Stochastic Principles of Optimality in Games with Nature and Their Application in Investment Management 14

Stochastic Principles of Optimality in Games with Nature and...

引用

14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Gorelik, Victor Zolotova, Tatiana Moscow Pedagogical State University Department of Simulation Systems and Operations Research FRC CSC RAS Moscow Russia Financial University Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9781665412308

The principle of optimality in decision-making for games with nature, based on assessments of efficiency and risk, is proposed. In contrast to the traditional approach to the definition of a mixed strategy in game theory, this work considers the possibility of a correlation dependence of random values of payoffs for the original alternatives. © 2021 IEEE.

关键词： Game theory

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Using Convolutional Neural Network to Classify 2D EEG Scalp Topograms during Visual Task 5

Using Convolutional Neural Network to Classify 2D EEG Scalp ...

引用

5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Korchagin, Sergey Maksimenko, Vladimir Hramov, Alexander Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia Institute of Information Technologies Mathematics and Mechanics Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

we developed an artificial neural network (ANN) classifier to analyze the cortical activity signals during visual information processing. We tested several ANN architectures and chose a convolutional neural network (CNN) with the Resnet50 topology. As a result, the trained CNN classifier achieved an accuracy of 74% on the data of newly recruited subjects. © 2021 IEEE

关键词： Convolutional neural networks

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Application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance 16

Application of the Semantic Segmentation Method in the Asses...

引用

16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Tulokhonova, Inna Mikhaylova, Svetlana Danilova, Soelma East Siberia State University of Technology and Management Department of Software Engineering and Artificial Intelligence Ulan-Ude Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

This article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. The concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. The main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. The proposed convolutional neural network (CNN) model is based on the U-Net architecture. This architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. The input data of the model is a set of satellite images of the forest area. The images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition. © 2023 IEEE.

关键词： Global warming

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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix 17

Method for Finding an Investment Strategy in the Case of a S...

引用

17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Gorelik, Victor Zolotova, Tatiana Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Simulation Systems and Operations Research Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息

ISBN: (纸本)9798350375718

关键词： Marketplaces

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Dynamic Approach to the analysis of Financial Structure: Overcoming the Bank-Based vs Market-Based Dichotomy 16

Dynamic Approach to the Analysis of Financial Structure: Ove...

引用

16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Krinichansky, Konstantin Grineva, Natalia Financial University under the Government of the Russian Federation Institute of Financial Studies Department of Financial Markets and Financial Engineering Moscow Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

A conceptual approach to the typology of countries' financial development has been developed, which will allow government authorities to make more accurate macroeconomic forecasts and apply a strategic approach to economic development planning. Modeling was conducted to analyze the impact of macroeconomic indicators, dummy variables of economic crises, and the technological component variable on indicators of financial development and variables characterizing the structure of the financial system. © 2023 IEEE.

关键词： Commerce

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Investor Risk Profile Determination Model 16

Investor Risk Profile Determination Model

引用

16th International Conference Management of Large-Scale System Development, MLSD 2023

ISBN: (纸本)9798350337907

An assessment of the investor's risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market. © 2023 IEEE.

关键词： Convolution

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Diverse subgroup set discovery

Diverse subgroup set discovery

引用

作者： Van Leeuwen, Matthijs Knobbe, Arno Machine Learning Department of Computer Science Katholieke Universiteit Leuven Leuven Belgium Algorithmic Data Analysis Department of Information and Computer Sciences Universiteit Utrecht Utrecht Netherlands Leiden Institute of Advanced Computer Science Universiteit Leiden Leiden Netherlands

Large data is challenging for most existing discovery algorithms, for several reasons. First of all, such data leads to enormous hypothesis spaces, making exhaustive search infeasible. Second, many variants of essentially the same pattern exist, due to (numeric) attributes of high cardinality, correlated attributes, and so on. This causes top-k mining algorithms to return highly redundant result sets, while ignoring many potentially interesting results. These problems are particularly apparent with subgroup discovery (SD) and its generalisation, exceptional model mining. To address this, we introduce subgroup set discovery: one should not consider individual subgroups, but sets of subgroups. We consider three degrees of redundancy, and propose corresponding heuristic selection strategies in order to eliminate redundancy. By incorporating these (generic) subgroup selection methods in a beam search, the aim is to improve the balance between exploration and exploitation. The proposed algorithm, dubbed DSSD for diverse subgroup set discovery, is experimentally evaluated and compared to existing approaches. For this, a variety of target types with corresponding datasets and quality measures is used. The subgroup sets that are discovered by the competing methods are evaluated primarily on the following three criteria: (1) diversity in the subgroup covers (exploration), (2) the maximum quality found (exploitation), and (3) runtime. The results show that DSSD outperforms each traditional SD method on all or a (non-empty) subset of these criteria, depending on the specific setting. The more complex the task, the larger the benefit of using our diverse heuristic search turns out to be. © The Author(s) 2012.

关键词： Redundancy

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Using Control Charts with Variable Control Limits in Statistical Process Control

Using Control Charts with Variable Control Limits in Statist...

引用

International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Marina Zhuravlyova Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

The procedure of building control charts with variable control limits based on nonparametric bootstrap percentile method is proposed. It makes possible to construct control chart of any statistic with unknown continuous distribution and involves a large number of calculations. The order of building one of the scalar statistics to be monitored for the group of mutually dependent quality characteristics is considered. Formulas for obtaining variable control limits based on percentile method are shown. The issue of identifying the structure of relationships between quality characteristics is raised. Proposed control chart, Hotelling T2 and Shewhart usual control charts and their modifications for variable control limits have been constructed using actual data of two characteristics of the rolled steel manufacturing process. It is concluded that the development of the proposed approach can be useful in statistical process control.

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Methodology for Training data Science Skills Based on Competitions on the Kaggle Platform

Methodology for Training Data Science Skills Based on Compet...

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Information Computing and Processing (ICP), Seminar on

作者： Zaur Kh. Kalazhokov Yan T. Makoveichuk Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia

The article describes the experience of teaching students of Big data analytics-related fields the basics of data science. The teaching methodology used at the Financial University under the Government of the Russian Federation is given, which consists in involvement of students of the group to competitions on Google Kaggle platform. In this methodology, the positive factors influencing the rapid acquisition of initial data science skills are: competitive effect; availability of prepared datasets and problem formulations from the real business sector in various directions; possibility of instant verification of the found solution; practical unlimited number of solutions. These factors contribute to the rapid understanding by students of the need to understand the essence of the problem for the best results, obligatory familiarization with the data, data cleaning and formatting, exploratory data analysis, forming a model with some basic level and its improvement, as well as interpretation.

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