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检索条件"机构=Data Analysis and Machine Learning"
200 条 记 录,以下是41-50 订阅
排序:
Using Control Charts with Variable Control Limits in Statistical Process Control
Using Control Charts with Variable Control Limits in Statist...
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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 continuo...
来源: 评论
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 ...
来源: 评论
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...
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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
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... 详细信息
来源: 评论
ON SECOND-ORDER SCORING RULES FOR EPISTEMIC UNCERTAINTY QUANTIFICATION
arXiv
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arXiv 2023年
作者: Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium
It is well known that accurate probabilistic predictors can be trained through empirical risk minimisation with proper scoring rules as loss functions. While such learners capture so-called aleatoric uncertainty of pr... 详细信息
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Investor Risk Profile Determination Model  16
Investor Risk Profile Determination Model
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16th International Conference Management of Large-Scale System Development, MLSD 2023
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 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 opti... 详细信息
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Determining the Genre of Music from a Melody Using machine learning and Parallel data Processing
Determining the Genre of Music from a Melody Using Machine L...
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Information Computing and Processing (ICP), Seminar on
作者: Danila A. Solodennikov Krystina A. Makoveichuk Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia
This article describes the author’s methodology for determining the genre of music from a melody using machine learning and parallel data processing. For determination is used dataset formed by the author, on the bas...
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Diverse subgroup set discovery
Diverse subgroup set discovery
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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 essenti... 详细信息
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PITFALLS OF EPISTEMIC UNCERTAINTY QUANTIFICATION THROUGH LOSS MINIMISATION
arXiv
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arXiv 2022年
作者: Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Germany Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium
Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The l... 详细信息
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Pitfalls in EEG-based brain effective connectivity analysis
Pitfalls in EEG-based brain effective connectivity analysis
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International Workshop on machine learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011
作者: Haufe, Stefan Nikulin, Vadim V. Nolte, Guido Müller, Klaus-Robert Berlin Institute of Technology Machine Learning Germany Bernstein Focus Neurotechnology Berlin Germany Neurophysics Charité University Medicine Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Intelligent Data Analysis Fraunhofer Institute FIRST Berlin Germany
We consider the problem of estimating brain effective connectivity from electroencephalographic (EEG) measurements, which is challenging due to instantaneous correlations in the sensor data caused by volume conduction... 详细信息
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learning invariances with stationary subspace analysis
Learning invariances with stationary subspace analysis
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2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
作者: Meinecke, Frank C. Von Bünau, Paul Kawanabe, Motoaki Müller, Klaus-R. Machine Learning Group Dept. Computer Science TU Berlin Franklinstr. 28/29 10587 Berlin Germany Intelligent Data Analysis Group Fraunhofer FIRST.IDA Kekuléstr. 7 12489 Berlin Germany
Recently, a novel subspace decomposition method, termed 'Stationary Subspace analysis' (SSA), has been proposed by Bünau et al. [10]. SSA aims to find a linear projection to a lower dimensional subspace s... 详细信息
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