Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Magda Gregorova"'
Publikováno v:
IEEE Access, Vol 12, Pp 184813-184826 (2024)
The increasing automation in the design process of electrical machines for vehicles generates huge amounts of data, leading to a growing interest in using machine learning for faster predictions and optimization. This paper presents an innovative app
Externí odkaz:
https://doaj.org/article/0c93983d2443497c9d880c18bce9f023
Publikováno v:
Autonomous Intelligent Systems, Vol 4, Iss 1, Pp 1-10 (2024)
Abstract The generation and optimization of simulation data for electrical machines remain challenging, largely due to the complexities of magneto-static finite element analysis. Traditional methodologies are not only resource-intensive, but also tim
Externí odkaz:
https://doaj.org/article/cb481940fcac4021800c9e5bdf4015f7
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030109271
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Proceedings. Part II
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Proceedings. Part II pp. 177-192
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Proceedings. Part II
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Proceedings. Part II pp. 177-192
We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33111f9455c4a891fd40fe2ac0cae244
https://doi.org/10.1007/978-3-030-10928-8_11
https://doi.org/10.1007/978-3-030-10928-8_11
Autor:
Magda Gregorova
Publikováno v:
Magda Gregorova
In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models. There are multiple reasons for promoting sparsity in the predictive models. By relying on a limited set of input variabl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a4679dc91bf7ac4711fb0c044f80c17
Publikováno v:
Entropy
Volume 22
Issue 8
Entropy, Vol 22, Iss 888, p 888 (2020)
Volume 22
Issue 8
Entropy, Vol 22, Iss 888, p 888 (2020)
One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70f7c3658346621a06d98e54888f8025
https://doi.org/10.3390/e22080888
https://doi.org/10.3390/e22080888
Publikováno v:
Machine Learning and Knowledge Discovery in Databases
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2017, Proceedings, Part II pp. 544-558
Machine Learning and Knowledge Discovery in Databases ISBN: 9783319712451
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2017, Proceedings, Part II
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Machine Learning and Knowledge Discovery in Databases
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2017, Proceedings, Part II pp. 544-558
Machine Learning and Knowledge Discovery in Databases ISBN: 9783319712451
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2017, Proceedings, Part II
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Machine Learning and Knowledge Discovery in Databases
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e83a68b55c5531b13700162539f1c830
Publikováno v:
Entropy, Vol 22, Iss 8, p 888 (2020)
One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the pr
Externí odkaz:
https://doaj.org/article/e1aaa28e2fba4819822de054f72380f5