Institute of Computer Science of the CAS, v. v. i.

Project results GA19-05704S

Records found: 27

0503755 - ÚI 2020 RIV US eng C - Proceedings Paper (International conf.)
Cabessa, Jérémie - Villa, A.
A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks.
IJCNN 2019. International Joint Conference on Neural Networks Proceedings. New York: IEEE, 2019, č. článku N-20311. ISBN 978-1-7281-1985-4.
[IJCNN 2019. International Joint Conference on Neural Networks /32./. Budapest (HU), 14.07.2019-19.07.2019]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: learning (artificial intelligence) * recurrent neural nets
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent link: http://hdl.handle.net/11104/0295541

0503688 - ÚI 2020 RIV CH eng C - Proceedings Paper (International conf.)
Cabessa, Jérémie - Šíma, Jiří
Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks.
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Proceedings, Part I. Cham: Springer, 2019 - (Tetko, I.; Kůrková, V.; Karpov, P.; Theis, F.), s. 806-818. Lecture Notes in Computer Science, 11727. ISBN 978-3-030-30486-7. ISSN 0302-9743.
[ICANN 2019. International Conference on Artificial Neural Networks /28./. Munich (DE), 17.09.2019-19.09.2019]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Recurrent neural networks * Threshold circuits * Finite state automata * Synfire rings
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent link: http://hdl.handle.net/11104/0295498

0510528 - ÚI 2020 RIV US eng J - Journal Article
Cabessa, Jérémie
Turing Complete Neural Computation Based on Synaptic Plasticity.
PLoS ONE. Roč. 14, č. 10 (2019), č. článku e0223451. E-ISSN 1932-6203
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Action potentials * Language * Machine learning algorithms * Neurons * Recurrent neural networks * Synapses * Synaptic plasticity * Neural pathways
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 2.776, year: 2018
http://hdl.handle.net/11104/0300985
Permanent link: http://hdl.handle.net/11104/0300985

0518369 - ÚI 2020 RIV CZ eng L4 - Software
Jurica, Tomáš - Vidnerová, Petra - Kalina, Jan
Robust interquantile training of neural networks.
Interní kód: Quantile 1.0 ; 2019
Technical Parameters: Kód v Pythonu je samostatně spustitelný, vyžaduje instalaci TensorFlow, Keras, SciPy, NumPy, scikit-learn. Spuštění kódu je přímočaré podle dokumentace. Dostupné pod licencí MIT.
Ekonomické parametry: Software umožňuje uživateli provést alternativní trénování neuronových sítí, které je robustní vůči odlehlým hodnotám. Jde dosud o první takovou implementaci, která je dostupná. Software výrazně usnadňuje práci s neuronovými sítěmi, protože provádět jejich trénování nezávisle na přítomnosti odlehlých hodnot by jinak vyžadovalo značně komplikované a zdlouhavé postupy.
R&D Projects: GA ČR(CZ) GA19-05704S; GA TA ČR(CZ) TN01000024
Institutional Support: RVO:67985807
Keywords: neural network * regression * robustness * outliers
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://github.com/jankalinaUI/Quantile
Permanent link: http://hdl.handle.net/11104/0303525

0482536 - ÚI 2021 RIV CH eng M - Monograph chapter
Kainen, P.C. - Kůrková, Věra
Quasiorthogonal dimension.
Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy, etc. Methods and Their Applications. Cham: Springer, 2020 - (Kosheleva, O.; Shary, S.; Xiang, G.; Zapatrin, R.), s. 615-629. Studies in Computational Intelligence, 835. ISBN 978-3-030-31040-0
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent link: http://hdl.handle.net/11104/0277964

0508155 - ÚI 2020 CZ eng A - Abstract
Kalina, Jan - Tobišková, Nicole - Tichavský, Jan
A Nonparametric Bootstrap Comparison of Variances of Robust Regression Estimators.
37th International Conference on Mathematical Methods in Economics 2019: Book of Abstracts. České Budějovice: 37th International Conference on Mathematical Methods in Economics 2019, 2019. s. 17-17.
[MME 2019: International Conference on Mathematical Methods in Economics /37./. 11.09.2019-13.09.2019, České Budějovice]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Robustness * Linear regression * Outliers * Bootstrap * Least weighted squares
Permanent link: http://hdl.handle.net/11104/0299134

0509646 - ÚI 2020 RIV CZ eng C - Proceedings Paper (International conf.)
Kalina, Jan - Tobišková, Nicole - Tichavský, Jan
A Nonparametric Bootstrap Comparison of Variances of Robust Regression Estimators.
Conference Proceedings. 37th International Conference on Mathematical Methods in Economics 2019. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Economics, 2019 - (Houda, M.; Remeš, R.), s. 168-173. ISBN 978-80-7394-760-6.
[MME 2019: International Conference on Mathematical Methods in Economics /37./. České Budějovice (CZ), 11.09.2019-13.09.2019]
R&D Projects: GA ČR(CZ) GA19-05704S; GA ČR GA17-01251S
Institutional Support: RVO:67985807
Keywords: robustness * linear regression * outliers * bootstrap * least weighted squares
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://mme2019.ef.jcu.cz/files/conference_proceedings.pdf
Permanent link: http://hdl.handle.net/11104/0300321

0508153 - ÚI 2020 CZ eng A - Abstract
Kalina, Jan - Vidnerová, Petra
Implicitly Weighted Robust Estimation of Quantiles in Linear Regression.
37th International Conference on Mathematical Methods in Economics 2019: Book of Abstracts. České Budějovice: 37th International Conference on Mathematical Methods in Economics 2019, 2019. s. 17-17.
[MME 2019: International Conference on Mathematical Methods in Economics /37./. 11.09.2019-13.09.2019, České Budějovice]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: regression quantiles * robust regression * outliers * leverage points
Permanent link: http://hdl.handle.net/11104/0299135

0509648 - ÚI 2020 RIV CZ eng C - Proceedings Paper (International conf.)
Kalina, Jan - Vidnerová, Petra
Implicitly weighted robust estimation of quantiles in linear regression.
Conference Proceedings. 37th International Conference on Mathematical Methods in Economics 2019. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Economics, 2019 - (Houda, M.; Remeš, R.), s. 25-30. ISBN 978-80-7394-760-6.
[MME 2019: International Conference on Mathematical Methods in Economics /37./. České Budějovice (CZ), 11.09.2019-13.09.2019]
R&D Projects: GA ČR(CZ) GA19-05704S; GA ČR(CZ) GA18-23827S
Institutional Support: RVO:67985807
Keywords: regression quantiles * robust regression * outliers * leverage points
Subject RIV: BB - Applied Statistics, Operational Research
Obor OECD: Statistics and probability
https://mme2019.ef.jcu.cz/files/conference_proceedings.pdf
Permanent link: http://hdl.handle.net/11104/0300322

0502476 - ÚI 2020 RIV US eng M - Monograph chapter
Kalina, Jan
Mental Health Clinical Decision Support Exploiting Big Data.
Computational Methods and Algorithms for Medicine and Optimized Clinical Practice. Hershey: IGI Global, 2019 - (Chui, K.; Lytras, M.), s. 160-184. ISBN 978-1-5225-8244-1
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: big data * decision support * machine learning * supervised learning * mental health
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent link: http://hdl.handle.net/11104/0294415

0517255 - ÚI 2020 RIV CZ eng L4 - Software
Kalina, Jan - Tichavský, Jan - Tobišková, Nicole
Nonparametric bootstrap for estimating variability of robust regression estimators.
Interní kód: Nonparametric Bootstrap 1.0 ; 2019
Technical Parameters: Kód v programovacím jazyce R spustitelný samostatně podle dokumentace, která je součástí jednotlivých souborů. Spuštění vyžaduje knihovnu robustbase. Dostupné pod licencí MIT.
Ekonomické parametry: Software umožňuje uživateli odhadnout varianční matici robustních regresních odhadů pomocí neparametrického bootstrapu. Pro některé z odhadů by jiný způsob výpočtu byl značně složitý a vyžadoval by využít komerční software, pro LWS odhad není jiný způsob výpočtu ani známý. Software tak výrazně usnadňuje práci s robustními regresními odhady.
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: robust regression * nonparametric bootstrap * outliers * covariance matrix
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://github.com/jankalinaUI/Bootstrap-LWS
Permanent link: http://hdl.handle.net/11104/0302552

0522581 - ÚI 2021 RIV SK eng J - Journal Article
Kalina, Jan - Tichavský, Jan
On Robust Estimation of Error Variance in (Highly) Robust Regression.
Measurement Science Review. Roč. 20, č. 1 (2020), s. 6-14. ISSN 1335-8871
R&D Projects: GA ČR(CZ) GA19-05704S
GA ČR(CZ) GA17-07384S
Institutional Support: RVO:67985807
Keywords: high robustness * robust regression * outliers * variance of errors * least weighted squares * simulation
Subject RIV: BB - Applied Statistics, Operational Research
Obor OECD: Statistics and probability
Impact factor: 1.122, year: 2018
http://hdl.handle.net/11104/0307056
Permanent link: http://hdl.handle.net/11104/0307056

0522365 - ÚI 2021 C - Proceedings Paper (International conf.)
Kalina, Jan - Vidnerová, Petra
Regression Neural Networks with a Highly Robust Loss Function.
Amistat 2019. Springer, 2020.
[AMISTAT 2019: Analytical Methods in Statistics. Liberec (CZ), 16.09.2019-19.09.2019]
R&D Projects: GA ČR(CZ) GA19-05704S; GA ČR(CZ) GA18-23827S
Institutional Support: RVO:67985807
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent link: http://hdl.handle.net/11104/0306871

0506360 - ÚI 2020 RIV CH eng C - Proceedings Paper (International conf.)
Kalina, Jan - Vidnerová, Petra
Robust Training of Radial Basis Function Neural Networks.
Artificial Intelligence and Soft Computing. Proceedings, Part I. Cham: Springer, 2019 - (Rutkowski, L.; Scherer, R.; Korytkowski, M.; Pedrycz, W.; Tadeusiewicz, R.; Zurada, J.), s. 113-124. Lecture Notes in Computer Science, 11508. ISBN 978-3-030-20911-7. ISSN 0302-9743.
[ICAISC 2019: International Conference on Artificial Intelligence and Soft Computing /18./. Zakopane (PL), 16.06.2019-20.06.2019]
R&D Projects: GA ČR(CZ) GA19-05704S; GA ČR(CZ) GA18-23827S
Institutional Support: RVO:67985807
Keywords: Machine learning * Outliers * Robustness * Subset selection * Anomaly detection
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent link: http://hdl.handle.net/11104/0297617

0511819 - ÚI 2020 RIV SK eng J - Journal Article
Kalina, Jan - Tichavský, Jan
Statistical learning for recommending (robust) nonlinear regression methods.
Journal of applied mathematics, statistics and informatics. Roč. 15, č. 2 (2019), s. 47-59. ISSN 1336-9180
R&D Projects: GA ČR(CZ) GA19-05704S
GA ČR(CZ) GA17-07384S
Institutional Support: RVO:67985807
Keywords: Statistical learning * Nonlinear regression * Robustness * Heteroscedasticity * nonlinear least weighted squares * optimal method selection * optimization * computations
Subject RIV: BB - Applied Statistics, Operational Research
Obor OECD: Statistics and probability
http://hdl.handle.net/11104/0302064
Permanent link: http://hdl.handle.net/11104/0302064

0507969 - ÚI 2020 RIV CH eng C - Proceedings Paper (International conf.)
Kůrková, Věra
Probabilistic Bounds for Approximation by Neural Networks.
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Proceedings, Part I. Cham: Springer, 2019 - (Tetko, I.; Kůrková, V.; Karpov, P.; Theis, F.), s. 418-428. Lecture Notes in Computer Science, 11727. ISBN 978-3-030-30486-7. ISSN 0302-9743.
[ICANN 2019. International Conference on Artificial Neural Networks /28./. Munich (DE), 17.09.2019-19.09.2019]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Approximation of random functions * Feedforward networks * Dictionaries of computational units * High-dimensional geometry * Concentration of measure * Azuma-Hoeffding inequalities
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent link: http://hdl.handle.net/11104/0298932

0508148 - ÚI 2021 GB eng J - Journal Article
Marozzi, M. - Mukherjee, A. - Kalina, Jan
Interpoint distance tests for high-dimensional comparison studies.
Journal of Applied Statistics. Roč. 47, č. 4 (2020), s. 653-665. ISSN 0266-4763
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Multivariate data * high dimensionality * nonparametric testing * interpoint distances * robustness
Subject RIV: BB - Applied Statistics, Operational Research
Obor OECD: Statistics and probability
Impact factor: 0.767, year: 2018
Marozzi, M.
Permanent link: http://hdl.handle.net/11104/0299132

0517845 - ÚI 2020 RIV CZ eng C - Proceedings Paper (International conf.)
Mráz, F. - Otto, F. - Pardubská, D. - Plátek, Martin
Lexicalized Syntactic Analysis by Restarting Automata.
Proceedings of the Prague Stringology Conference 2019. Prague: Czech Technical University in Prague, 2019 - (Holub, J.; Žďárek, J.), s. 69-83. ISBN 978-80-01-06618-8.
[Prague Stringology Conference 2019. Prague (CZ), 26.08.2019-28.08.2019]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Restarting automaton * h-lexicalization * lexical disambiguation
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://www.stringology.org/papers/PSC2019.pdf
Permanent link: http://hdl.handle.net/11104/0303097

0517853 - ÚI 2021 DE eng J - Journal Article
Mráz, F. - Otto, F. - Pardubská, D. - Plátek, Martin
Lexicalized Syntactic Analysis by Two-Way Restarting Automata.
Journal of Automata, Languages and Combinatorics. -, Submitted October 2019 (2020). ISSN 1430-189X
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Restarting automaton * h-lexicalization * lexical disambiguation
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent link: http://hdl.handle.net/11104/0303102

0517855 - ÚI 2020 RIV DE eng C - Proceedings Paper (International conf.)
Plátek, Martin - Mráz, F. - Pardubská, D.
Lexically Syntactic Characterization by Restarting Automata.
ITAT 2019: Information Technologies – Applications and Theory. Aachen: Technical University & CreateSpace Independent Publishing, 2019 - (Barančíková, P.; Holeňa, M.; Horváth, T.; Pleva, M.; Rosa, R.), s. 104-111. CEUR Workshop Proceeding, 2473. ISSN 1613-0073.
[ITAT 2019: Conference Information Technologies - Applications and Theory /19./. Donovaly (SK), 20.09.2019-24.09.2019]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Restarting automaton * lexicalized syntax * correctness preserving property
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2473/paper1.pdf
Permanent link: http://hdl.handle.net/11104/0303106

0507515 - ÚI 2021 GB eng J - Journal Article
Šíma, Jiří
Analog Neuron Hierarchy.
Neural Networks. -, Submitted August 2019 (2019). ISSN 0893-6080
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: recurrent neural network * analog neuron hierarchy * deterministic context-free language * Turing machine * Chomsky hierarchy
Impact factor: 5.785, year: 2018
Permanent link: http://hdl.handle.net/11104/0298502

0502583 - ÚI 2020 RIV CH eng C - Proceedings Paper (International conf.)
Šíma, Jiří
Counting with Analog Neurons.
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Proceedings, Part I. Cham: Springer, 2019 - (Tetko, I.; Kůrková, V.; Karpov, P.; Theis, F.), s. 389-400. Lecture Notes in Computer Science, 11727. ISBN 978-3-030-30486-7. ISSN 0302-9743.
[ICANN 2019. International Conference on Artificial Neural Networks /28./. Munich (DE), 17.09.2019-19.09.2019]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Neural computing * Analog state * Deterministic pushdown automaton * Deterministic context-free language * Chomsky hierarchy
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent link: http://hdl.handle.net/11104/0294486

0505945 - ÚI 2020 RIV DE eng C - Proceedings Paper (International conf.)
Šíma, Jiří - Plátek, Martin
One Analog Neuron Cannot Recognize Deterministic Context-Free Languages.
Neural Information Processing. Proceedings, Part III. Heidelberg: Springer, 2019 - (Gedeon, T.; Wong, K.; Lee, M.), s. 77-89. Lecture Notes on Computer Science, 11955. ISBN 978-3-030-36717-6.
[ICONIP 2019. International Conference on Neural Information Processing of the Asia-Pacific Neural Network /26./. Sydney (AU), 12.12.2019-15.12.2019]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: Neural computing * Analog neuron hierarchy * Deterministic context-free language * Restart automaton * Chomsky hierarchy
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://www.springer.com/gp/book/9783030367176
Permanent link: http://hdl.handle.net/11104/0297270

0490203 - ÚI 2020 RIV GB eng J - Journal Article
Šíma, Jiří
Subrecursive Neural Networks.
Neural Networks. Roč. 116, August (2019), s. 208-223. ISSN 0893-6080
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: recurrent neural network * Chomsky hierarchy * cut language * quasi-periodic number
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 5.785, year: 2018
http://dx.doi.org/10.1016/j.neunet.2019.04.019
Šíma, Jiří
Permanent link: http://hdl.handle.net/11104/0284481

0517237 - ÚI 2020 RIV CZ eng L4 - Software
Tichavský, Jan - Kalina, Jan
Residual and nonparametric bootstrap for estimating variability of robust regression estimators.
Interní kód: Bootstrap Residual 1.0 ; 2019
Technical Parameters: Kód v Matlabu, spustitelný samostatně podle instrukcí v dokumentaci. Spuštění vyžaduje kód pro výpočet LTS a LWS odhadu. Dostupné pod licencí MIT.
Ekonomické parametry: Software umožňuje uživateli odhadnout varianční matici pro LTS a LWS odhady pomocí reziduálního bootstrapu. Software usnadňuje práci s těmito odhady, protože jiná metoda pro odhad jejich variability není dosud nikde implementovaná.
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: robust regression * residual bootstrap * nonparametric bootstrap * outliers * covariance matrix
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://github.com/Veragin/Bootstrap
Permanent link: http://hdl.handle.net/11104/0302549

0518361 - ÚI 2020 RIV CZ eng L4 - Software
Tichavský, Jan - Kalina, Jan
Robust MWCD (minimum weighted covariance determinant) estimator for multivariate data.
Interní kód: MWCD 1.0 ; 2019
Technical Parameters: Kód v Matlabu, spustitelný samostatně podle dokumentace, která je součástí jednotlivých souborů. Spuštění vyžaduje knihovnu fastmcd.m. Dostupné pod licencí MIT.
Ekonomické parametry: Jde o dosud první veřejně dostupnou implementaci kódu pro výpočet MWCD odhadu. Software tak výrazně usnadňuje analýzu mnohorozměrných dat pomocí nástrojů robustních vůči odlehlým hodnotám.
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: multivariate data * robustness * breakdown point * outliers
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://github.com/Veragin/MWCDcode
Permanent link: http://hdl.handle.net/11104/0303517

0518359 - ÚI 2020 RIV CZ eng L4 - Software
Tichavský, Jan - Kalina, Jan
The least weighted squares estimator in linear regression.
Interní kód: LWS 1.0 ; 2019
Technical Parameters: Kód v Matlabu, spustitelný samostatně podle dokumentace, která je součástí jednotlivých souborů. Spuštění vyžaduje knihovnu fastlts.m. Dostupné pod licencí MIT.
Ekonomické parametry: Jde o dosud první veřejně dostupnou implementaci kódu pro výpočet LWS odhadu v regresi. Software tak výrazně usnadňuje regresní modelování pomocí nástrojů robustních vůči odlehlým hodnotám. Současně lze tuto implementaci LWS odhadu, která je obecná, použít i v kontextu neuronových sítí, kde by byl jiný způsob výpočtu značně složitý.
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional Support: RVO:67985807
Keywords: linear regression * robustness * breakdown point * outiers
Subject RIV: IN - Informatics, Computer Science
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://github.com/Veragin/LWScode
Permanent link: http://hdl.handle.net/11104/0303514