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Zapytanie: KORDOS M
Liczba odnalezionych rekordów: 26



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1/26
Nr opisu: 0000126741   
Data compression measures for meta-learning systems.
[Aut.]: Marcin Blachnik, M. Kordos, Sławomir Golak.
W: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, September 9-12, 2018, Poznan, Poland. Eds. Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. Piscataway : Institute of Electrical and Electronics Engineers, 2018, s. 25-28, bibliogr. 18 poz. (Annals of Computer Science and Information Systems ; vol. 15 2300-5963)

2/26
Nr opisu: 0000127990   
Data set partitioning in evolutionary instance selection.
[Aut.]: M. Kordos, Ł. Czepielik, Marcin Blachnik.
W: Intelligent data engineering and automated learning. IDEAL 2018, 19th International conference, Madrid, Spain, November 21-23, 2018. Proceedings. Pt. 1. Eds. Hujun Yin, David Camacho, Paulo Novais, Antonio J. Tallon-Ballesteros. Cham : Springer Verlag, 2018, s. 631-641, bibliogr. 21 poz. (Lecture Notes in Computer Science ; vol. 11314 Lecture Notes in Artificial Intelligence ; 0302-9743)

3/26
Nr opisu: 0000104423   
Fusion of instance selection methods in regression tasks.
[Aut.]: A. Arnaiz-Gonzalez, Marcin Blachnik, M. Kordos, C. Garcia-Osorio.
-Inf. Fusion 2016 vol. 30, s. 69-79, bibliogr. 42 poz.. Impact Factor 5.667. Punktacja MNiSW 45.000

wybór instancji ; regresja

instance selection ; regression ; ensemble models

4/26
Nr opisu: 0000107059
Information Selection and Data Compression RapidMiner Library.
[Aut.]: Marcin Blachnik, M. Kordos.
W: Machine intelligence and big data in industry. Eds. Dominik Ryżko, Piotr Gawrysiak, Marzena Kryszkiewicz, Henryk Rybiński. [B.m.] : Springer, 2016, s. 135-145, bibliogr. 18 poz. (Studies in Big Data ; vol. 19 2197-6503)

5/26
Nr opisu: 0000104539   
Shaping inductor geometry for casting functionally graded composites.
[Aut.]: Sławomir Golak, M. Kordos.
-COMPEL 2016 vol. 35 no. 1, s. 16-26, bibliogr. 12 poz.. Impact Factor 0.487. Punktacja MNiSW 15.000

modelowanie matematyczne ; optymalizacja ; zjawiska sprzężone ; magnetohydrodynamika ; materiały z funkcjonalną gradacją własności ; optymalizacja numeryczna

mathematical modelling ; optimization ; coupled phenomena ; magnetohydrodynamics ; functionally graded materials ; numerical optimization

6/26
Nr opisu: 0000105020   
Noise reduction in regression tasks with distance, instance, attribute and density weighting.
[Aut.]: M. Kordos, Andrzej Rusin, Marcin Blachnik.
W: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF). CYBCONF 2015, June 24-26, 2015, Gdynia, Poland. Proceedings. Eds. Piotr Jędrzejowicz [et al.]. Piscataway : Institute of Electrical and Electronics Engineers, 2015, s. 73-78, bibliogr. 22 poz.

wybór instancji ; sieć neuronowa ; redukcja szumów

instance selection ; neural network ; noise reduction

7/26
Nr opisu: 0000092181
Bagging of instance selection algorithms.
[Aut.]: Marcin Blachnik, M. Kordos.
W: Artificial intelligence and soft computing. ICAISC 2014. 13th International conference, Zakopane, Poland, June 1-5, 2014. Proceedings. Pt. 2. Eds. L. Rutkowski [et al.]. Berlin : Springer, 2014, s. 40-51, bibliogr. 20 poz. (Lecture Notes in Computer Science ; vol. 8468 Lecture Notes in Artificial Intelligence ; 0302-9743)

8/26
Nr opisu: 0000099548
Instance selection in logical rule extraction for regression problems.
[Aut.]: M. Kordos, Sz. Białka, Marcin Blachnik.
W: Artificial intelligence and soft computing. ICAISC 2013. 12th International conference, Zakopane, Poland, June 9-13, 2013. Proceedings. Pt. 2. Eds. L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, J. M. Zurada. Berlin : Springer, 2013, s. 167-175 bibliogr. 19 poz. (Lecture Notes in Computer Science ; vol. 7895 0302-9743)

9/26
Nr opisu: 0000099549
Instance selection in RapidMiner.
[Aut.]: Marcin Blachnik, M. Kordos.
W: RapidMiner. Data mining use cases and business analytics applications. Eds. Markus Hofmann, Ralf Klinkenberg. Boca Raton : CRC Press-Taylor & Francis Group, 2013, s. 377-406

10/26
Nr opisu: 0000091621
Optimization of inductor geometry for the casting process of functionally graded composites.
[Aut.]: Sławomir Golak, M. Kordos.
W: Proceedings of International Symposium on Heating by Eletromagnetic Sources, Padua, Italy, May 21-24, 2013. [B.m.] : [b.w.], 2013, s. 321-328

11/26
Nr opisu: 0000081178   
Combining the advantages of neural networks and decision trees for regression problems in a steel temperature prediction system.
[Aut.]: M. Kordos, P. Kania, P. Budzyna, Marcin Blachnik, Tadeusz Wieczorek, Sławomir Golak.
W: Hybrid artificial intelligent systems. HAIS 2012. 7th International conference, Salamanca, Spain, March 28-30th, 2012. Proceedings. Pt 2. Eds: E. Corchado [et al.]. Berlin : Springer, 2012, s. 36-45, bibliogr. 16 poz. (Lecture Notes in Computer Science ; nr 7209 0302-9743)

sieć neuronowa ; drzewo decyzyjne ; regresja ; reguła logiczna

neural network ; decision tree ; regression ; logical rule

12/26
Nr opisu: 0000081106   
Computational complexity reduction and interpretability improvement of distance-based decision trees.
[Aut.]: Marcin Blachnik, M. Kordos.
W: Hybrid artificial intelligent systems. HAIS 2012. 7th International conference, Salamanca, Spain, March 28-30th, 2012. Proceedings. Pt 1. Eds: E. Corchado [et al.]. Berlin : Springer, 2012, s. 288-297, bibliogr. 19 poz. (Lecture Notes in Computer Science ; nr 7208 0302-9743)

grupowanie ; drzewo decyzyjne

clustering ; decision tree

13/26
Nr opisu: 0000081109   
Evolutionary optimized forest of regression trees: application in metallurgy.
[Aut.]: M. Kordos, J. Piotrowski, Sz. Białka, Marcin Blachnik, Sławomir Golak, Tadeusz Wieczorek.
W: Hybrid artificial intelligent systems. HAIS 2012. 7th International conference, Salamanca, Spain, March 28-30th, 2012. Proceedings. Pt 1. Eds: E. Corchado [et al.]. Berlin : Springer, 2012, s. 409-420, bibliogr. 26 poz. (Lecture Notes in Computer Science ; nr 7208 0302-9743)

drzewo decyzyjne ; regresja ; optymalizacja ewolucyjna

decision tree ; regression ; evolutionary optimization

14/26
Nr opisu: 0000081103   
Extraction of prototype-based threshold rules using neural training procedure.
[Aut.]: Marcin Blachnik, M. Kordos.
W: Artificial neural networks and machine learning. ICANN 2012, 22th International conference, Lausanne, Switzerland, September 11-14, 2012. Proceedings. Pt 2. Eds: A. Villa [et al.]. Berlin : Springer, 2012, s. 255-262, bibliogr. 14 poz. (Lecture Notes in Computer Science ; vol. 7553 0302-9743)

data understanding ; rule extraction ; prototype-based rules

15/26
Nr opisu: 0000081105   
Instance selection with neural networks for regression problems.
[Aut.]: M. Kordos, Marcin Blachnik.
W: Artificial neural networks and machine learning. ICANN 2012, 22th International conference, Lausanne, Switzerland, September 11-14, 2012. Proceedings. Pt 2. Eds: A. Villa [et al.]. Berlin : Springer, 2012, s. 263-270, bibliogr. 16 poz. (Lecture Notes in Computer Science ; vol. 7553 0302-9743)

sieć neuronowa ; regresja ; wybór instancji

neural network ; regression ; instance selection

16/26
Nr opisu: 0000084632   
Rational design of small molecule inhibitors targeting the rac GTPase-p67phox signaling axis in inflammation.
[Aut.]: E. Bosco, S. Kumar, F. Marchioni, Jacek* Biesiada, M. Kordos, K. Szczur, J. Meller, W. Seibel, A. Mizrahi, E. Pick, M. Filippi, Y. Zheng.
-Chem. Biol. 2012 vol. 19 no. 2, s. 228-242, bibliogr.. Impact Factor 6.157. Punktacja MNiSW 40.000

17/26
Nr opisu: 0000081110
Selecting representative prototypes for prediction the oxygen activity in electric arc furnace.
[Aut.]: Marcin Blachnik, M. Kordos, Tadeusz Wieczorek, Sławomir Golak.
W: Artificial intelligence and soft computing. ICAISC 2012. 11th International conference, Zakopane, Poland, April 29 - May 3, 2012. Pt 2. Eds: Leszek Rutkowski [et al.]. Berlin : Springer, 2012, s. 539-547, bibliogr. 12 poz. (Lecture Notes in Computer Science ; vol. 7268 0302-9743)

regresja ; metoda najbliższego sąsiedzwa ; zastosowanie przemysłowe ; elektryczny piec łukowy

regression ; nearest neighbour method ; industrial application ; electric arc furnace

18/26
Nr opisu: 0000081116   
A hybrid system with regressiont trees in steel-making process.
[Aut.]: M. Kordos, Marcin Blachnik, M. Perzyk, J. Kozłowski, O. Bystrzycki, M. Gródek, A. Byrdziak, Z. Motyka.
W: Hybrid artificial intelligent systems. HAIS 2011. 6th International conference, Wroclaw, Poland, May 23-25, 2011. Proceedings. Pt 1. Eds: E. Corchado, M. Kurzyński, M. Woźniak. Berlin : Springer, 2011, s. 222-230, bibliogr. 13 poz. (Lecture Notes in Computer Science ; vol. 6678 Lecture Notes in Artificial Intelligence ; 0302-9743)

19/26
Nr opisu: 0000081123   
Evolutionary optimization of regression model ensembles in steel-making process.
[Aut.]: M. Kordos, Marcin Blachnik, Tadeusz Wieczorek.
W: Intelligent data engineering and automated learning. IDEAL 2011. 12th International conference, Norwich, UK, September 7-9, 2011. Proceedings. Eds: H. Yin, W. Wang, V. J. Rayward-Smith. Berlin : Springer, 2011, s. 369-376, bibliogr. 15 poz. (Lecture Notes in Computer Science ; vol. 6936 0302-9743)

sieć neuronowa ; algorytm ewolucyjny

neural network ; evolutionary algorithm

20/26
Nr opisu: 0000082101
Instance selection and prototype based rules. A new extension to RapidMiner.
[Aut.]: Marcin Blachnik, M. Kordos.
W: Proceedings of the 2nd RapidMiner Community Meeting and Conference. RCOMM 2011, [Dublin, 7-10.06.2011]. Eds: S. Fischer, I. Mierswa. Aachen : Shaker, 2011, s. 21-30, bibliogr. 18 poz.

21/26
Nr opisu: 0000081121   
Neural network committees optimized with evolutionary methods for steel temperature control.
[Aut.]: M. Kordos, Marcin Blachnik, Tadeusz Wieczorek, Sławomir Golak.
W: Computational collective intelligence. Technologies and applications. ICCCI 2011. Third international conference, Gdynia, Poland, September 21-23, 2011. Proceedings. Pt 1. Eds: P. Jędrzejewicz, N.T. Nguyen, K. Hoang. Berlin : Springer, 2011, s. 42-51, bibliogr. 19 poz. (Lecture Notes in Computer Science ; vol. 6922 Lecture Notes in Artificial Intelligence ; 0302-9743)

algorytm ewolucyjny ; regresja ; metalurgia ; elektryczny piec łukowy

evolutionary algorithm ; regression ; metallurgy ; electric arc furnace

22/26
Nr opisu: 0000081125   
Simplifying SVM with weighted LVQ algorithm.
[Aut.]: Marcin Blachnik, M. Kordos.
W: Intelligent data engineering and automated learning. IDEAL 2011. 12th International conference, Norwich, UK, September 7-9, 2011. Proceedings. Eds: H. Yin, W. Wang, V. J. Rayward-Smith. Berlin : Springer, 2011, s. 212-219, bibliogr. 12 poz. (Lecture Notes in Computer Science ; vol. 6936 0302-9743)

SVM ; algorytm LVQ

SVM ; LVQ algorithm

23/26
Nr opisu: 0000081119   
Temperature prediction in electric arc furnace with neural network tree.
[Aut.]: M. Kordos, Marcin Blachnik, Tadeusz Wieczorek.
W: Artificial neural networks and machine learning. ICANN 2011, 21th International conference, Espoo, Finland, June 14-17, 2011. Proceedings. Pt 2. Eds: T. Honkela [et al.]. Berlin : Springer, 2011, s. 71-78, bibliogr. 18 poz. (Lecture Notes in Computer Science ; vol. 6792 0302-9743)

24/26
Nr opisu: 0000063307   
Do we need whatever more than k-NN?.
[Aut.]: M. Kordos, Marcin Blachnik, D. Strzempa.
W: Artificial intelligence and soft computing. ICAISC 2010. 10th International conference, Zakopane, Poland, June 13-17, 2010. Pt 1. Eds: L. Rutkowski [et al.]. Berlin : Springer, 2010, s. 414-421, bibliogr. 22 poz. (Lecture Notes in Computer Science ; vol. 6113 0302-9743)

algorytm klasyfikacji ; k-NN ; metoda najbliższego sąsiedzwa

classification algorithms ; k-NN ; nearest neighbour method

25/26
Nr opisu: 0000063299   
Information theory vs. correlation based feature ranking methods in application to metallurgical problem solving.
[Aut.]: Marcin Blachnik, A. Bukowiec, M. Kordos, Jacek* Biesiada.
W: Artificial intelligence and soft computing. ICAISC 2010. 10th International conference, Zakopane, Poland, June 13-17, 2010. Pt 1. Eds: L. Rutkowski [et al.]. Berlin : Springer, 2010, s. 289-298, bibliogr. 9 poz. (Lecture Notes in Computer Science ; vol. 6113 0302-9743)

metalurgia ; teoria informacji ; metoda wyboru cech

metallurgy ; information theory ; feature ranking method

26/26
Nr opisu: 0000069742
Neural network-based prediction of additives in the steel refinement process.
[Aut.]: Tadeusz Wieczorek, M. Kordos.
-Comput. Methods Mater. Sci. 2010 vol. 10 no. 1, s. 16-24, bibliogr. 24 poz.

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