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Page 4877 of Mathematical Reviews Vol. , Issue 2004f [page]

2004 Mathematical Reviews  
attributes in the discernibility matrix.” 2004f:68133 68T05 68T10 Wojnarski, Marcin (PL-WASWMI; Warsaw) LTF-C: architecture, training algorithm and applications of new neural classifier.  ...  Summary: “This paper presents a new model of an artificial neural network solving classification problems, called a Local Transfer Function Classifier (LTF-C).  ... 

Attribute Selection for EEG Signal Classification Using Rough Sets and Neural Networks [chapter]

Kenneth Revett, Marcin Szczuka, Pari Jahankhani, Vassilis Kodogiannis
2006 Lecture Notes in Computer Science  
This paper describes the application of rough sets and neural network models for classification of electroencephalogram (EEG) signals from two patient classes: normal and epileptic.  ...  These results were confirmed using standard neural network based classifiers.  ...  The authors wish to thank the authors of EEG dataset which is publicly available at http://www.meb.uni-bonn.de/epileptologie/science/physik/eegdata.html.  ... 
doi:10.1007/11908029_43 fatcat:avcrkjzzonaktodkh3y2qebnmu

On the Evolution of Rough Set Exploration System [chapter]

Jan G. Bazan, Marcin S. Szczuka, Arkadiusz Wojna, Marcin Wojnarski
2004 Lecture Notes in Computer Science  
We present the next version (ver. 2.1) of the Rough Set Exploration System -a software tool featuring a library of methods and a graphical user interface supporting variety of rough-set-based and related  ...  Methods, features and abilities of the implemented software are discussed and illustrated with examples in data analysis and decision support.  ...  Development of our software was supported by grants 4T11C04024 and 3T11C00226 from Polish Ministry of Scientific Research and Information Technology.  ... 
doi:10.1007/978-3-540-25929-9_73 fatcat:wmj2l7nuebe5jle2d4lcxhg2xa

Music Genre Recognition in the Rough Set-Based Environment [chapter]

Piotr Hoffmann, Bożena Kostek
2015 Lecture Notes in Computer Science  
Classification effectiveness employing rough sets is compared against k-Nearest Neighbors (k-NN) and Local Transfer function classifiers (LTF-C).  ...  Results obtained are analyzed in terms of global class recognition and also per genre.  ...  PBS1/B3/16/2012 entitled "Multimodal system supporting acoustic communication with computers" financed by the Polish National Centre for Research and Development and the company Intel Technology Poland  ... 
doi:10.1007/978-3-319-19941-2_36 fatcat:xgarfliqmbevlmxtcfmuyqheyi

The Rough Set Exploration System [chapter]

Jan G. Bazan, Marcin Szczuka
2005 Lecture Notes in Computer Science  
The main functionalities of this software system are presented along with a brief explanation of the algorithmic methods used by RSES.  ...  This article gives an overview of the Rough Set Exploration System (RSES). RSES is a freely available software system toolset for data exploration, classification support and knowledge discovery.  ...  Acknowledgement We would like to express our gratitude to all the current and previous members and supporters of RSES development team, in particular the creators of Rosetta [12, 42] -Aleksander Øhrn  ... 
doi:10.1007/11427834_2 fatcat:afsywv4yrbhorcm4oacwcjaxr4

Recent progress in wide-area surveillance: protecting our pipeline infrastructure

Vijayan K. Asari, Sidike Paheding, Chen Cui, Varun Santhaseelan, Qian Lin, Jan P. Allebach, Zhigang Fan
2015 Imaging and Multimedia Analytics in a Web and Mobile World 2015  
This method makes use of monogenic phase features into a cascade of pre-trained classifiers to eliminate unwanted regions.  ...  The pipeline industry has millions of miles of pipes buried along the length and breadth of the country.  ...  Figure 9 :Figure 10 : 910 Results of background elimination. (a) Original image, (b) LTFS, (c) local phase, and (d) LTFS+APS. The detection output of the proposed algorithm.  ... 
doi:10.1117/12.2086905 fatcat:zfohur6qmjh5zgno33cgnyccvi

Fast semantic parsing with well-typedness guarantees [article]

Matthias Lindemann, Jonas Groschwitz, Alexander Koller
2020 arXiv   pre-print
We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving  ...  AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks.  ...  We thank the anonymous reviewers and the participants of the DELPH-IN Summit 2020 for their helpful feedback and comments.  ... 
arXiv:2009.07365v2 fatcat:f7os7ey3yzgehgb6qtacqek6gu

Page 2343 of Mathematical Reviews Vol. , Issue Index [page]

Mathematical Reviews  
(English summary) Winfield, Mike see Leung, Wing Kai, (2004h:00017) and (2004h:00017) Wojna, Arkadiusz see Gora, Grzegorz, 2004g:68142 Wojnarski, Marcin LTF-C: architecture, training algorithm and applications  ...  of new neural classifier.  ... 

Page 2721 of Mathematical Reviews Vol. , Issue Index [page]

Mathematical Reviews  
(Summary) 2004j:68050 68P10 (68P15) Wojnarski, Marcin LTF-C: architecture, training algorithm and applications of new neural classifier. (English summary) Fund. Inform. 54 (2003), no. 1, 89-105.  ...  Sequence spaces and applications, 9-13, Narosa, New Delhi, 1999. (George Androulakis) 2004e:46033 46B45 (46B03) — Generalizations of the co-/\-/,, theorem of Bessaga and Petczynski.  ... 

Fast semantic parsing with well-typedness guarantees

Matthias Lindemann, Jonas Groschwitz, Alexander Koller
2020 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)   unpublished
We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving  ...  AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks.  ...  We thank the anonymous reviewers and the participants of the DELPH-IN Summit 2020 for their helpful feedback and comments.  ... 
doi:10.18653/v1/2020.emnlp-main.323 fatcat:z7a3lk6ndvehvbptguyldenasm