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Wyświetlanie 1-15 z 177
Tytuł:
SVM based classification method of railway`s defects
Autorzy:
Bojarczak, P.
Lesiak, P.
Powiązania:
https://bibliotekanauki.pl/articles/158025.pdf
Data publikacji:
2007
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
metoda magnetycznej pamięci metalu
transformata falkowa
sieci SVM
Method of Metal Magnetic Memory
wavelets transform
Opis:
Railway's surface defects belong to some kind of railway's flaws not been detected by traditional ultrasonic method and therefore they pose a major thread to the safety of railway traffic. Paper's aim is to present the Method of Metal Magnetic Memory along with SVM network allowing for detection of surface defects. Signals coming from the device whose operation is based on this method are given to wavelet's packet block extracting the most important features characterizing surface defects, followed by SVM network operating as a classifier.
W artykule przedstawiono próbę wykorzystania metody magnetycznej pamięci metalu wraz z klasyfikatorem opartym o sieć SVM (Support Vector Machines) do wykrywania wad powierzchniowych występujących w szynach kolejowych. Wady te są niewykrywalne przez tradycyjne metody oparte na ultradźwiękach a przez to stanowią poważne zagrożenie dla bezpieczeństwa ruchu pociągów.
Źródło:
Pomiary Automatyka Kontrola; 2007, R. 53, nr 12, 12; 15-17
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The diseases classification method on gait abnormalities characteristic contributions
Autorzy:
Chandzlik, S.
Piecha, J.
Powiązania:
https://bibliotekanauki.pl/articles/333759.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
klasyfikacja chorób neurologicznych
choroba Parkinsona
niedowład
udar niedokrwienny mózgu
automatyczne zakończenie
sieci nuronowe
neurological disease classification
Parkinson disease
hemiparesis
ischemic stroke
automatic conclusion
neural networks
Opis:
Present medicine uses computers in various applications, especially in a field of a diseases level classification and diagnosis. In many cases an automatic conclusion making units are the main goal of the computer systems usage. The software units are developed for the diseases classification or for monitoring of the disease medical treatment. An example application was described in this paper. It concerns a gait abnormalities level analysis that is described by a data records gathered by insoles of Parotec System for Windows (PSW) [17,18]. The PSW software package is used for visualisation of the gait characteristic static and dynamic characteristic features. In the authors' works many additional data components were distinguished. The field of the applications is located within the neurological gait characteristics also the source applications concern orthopaedics [16,18]. Careful analysis of the data provided the developers with new areas the PSW applications [4,11,13]. For conclusion making units the artificial networks theory was implemented [2,4,11,13]. For more effective training of the neural networks specific characteristic measures were introduced [4,5]. They allow controlling the training process more precisely, avoiding mistakes in current records classification.
Źródło:
Journal of Medical Informatics & Technologies; 2005, 9; 187-194
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Todim-Fse: A Multicriteria Classification Method Based On Prospect Theory
Autorzy:
Campos Passos, Aderson
Autran Monteiro Gomes, Luiz Flavio
Powiązania:
https://bibliotekanauki.pl/articles/578558.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Ekonomiczny w Katowicach
Tematy:
Metoda TOD IM-FSE
Wielokryterialna klasyfikacja kandydatów
Wielokryterialna analiza decyzyjna
TOD IM-FSE method
Prospect Theory
Multicriteria classification of candidates
Multiple Criteria Decision Analysis
Opis:
This paper introduces TODIM-FSE, a multicriteria method for classifying alternatives based on Prospect Theory. TODIM-FSE therefore relies on the TODIM method combined with the Fuzzy Synthetic Evaluation approach. TODIM-FSE makes use of the innovative "contribution" concept, not used previously for multicriteria classification purposes. This notion is central to the classification procedure of TODIM-FSE as it is associated to the contribution of each criterion to the classification of a given alternative in a predefined category. The TODIM-FSE method is explained in this paper by means of an application example and its steps are outlined. The application example has to do with the selection of trainee candidates for a company in the area of information technology. The classification of the candidates allows to identify the best of them, which is typically done at the first stage of the selection process. Some of the evaluation criteria considered in the study were: computers skills, mastery of technical English, and previous working experience in the field. In the second stage of that process another procedure ranks the best candidates. TODIM-FSE can be easily programmed in spreadsheets so as to be made available to professionals without a sound knowledge of either Multiple Criteria Decision Analysis or Prospect Theory. Currently the authors are working on a series of applications for validating TODIM-FSE in a broader way.
Źródło:
Multiple Criteria Decision Making; 2014, 9; 123-139
2084-1531
Pojawia się w:
Multiple Criteria Decision Making
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimal classification method for smiling vs neutral facial display recognition
Autorzy:
Nurzyńska, K.
Smołka, B.
Powiązania:
https://bibliotekanauki.pl/articles/333381.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
local binary patterns
support vector machines
k-nearest neighbourhood
template matching
lokalne wzorce binarne
maszyna wektorów nośnych
dopasowanie wzorców
Opis:
Human face depicts what happens in the soul, therefore correct recognition of emotion on the basis of facial display is of high importance. This work concentrates on the problem of optimal classification technique selection for solving the issue of smiling versus neutral face recognition. There are compared most frequently applied classification techniques: k-nearest neighbourhood, support vector machines, and template matching. Their performance is evaluated on facial images from several image datasets, but with similar image description methods based on local binary patterns. According to the experiments results the linear support vector machine gives the most satisfactory outcomes for all conditions.
Źródło:
Journal of Medical Informatics & Technologies; 2014, 23; 87-94
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effective multi-label classification method with applications to text document categorization
Autorzy:
Glinka, K.
Zakrzewska, D.
Powiązania:
https://bibliotekanauki.pl/articles/94735.pdf
Data publikacji:
2016
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
multilabel classification
text categorization
problem transformation method
text management
Opis:
Increasing number of repositories of online documents resulted in growing demand for automatic categorization algorithms. However, in many cases the texts should be assigned to more than one class. In the paper, new multi-label classification algorithm for short documents is considered. The presented problem transformation Labels Chain (LC) algorithm is based on relationship between labels, and consecutively uses result labels as new attributes in the following classification process. The method is validated by experiments conducted on several real text datasets of restaurant reviews, with different number of instances, taking into account such classifiers as kNN, Naive Bayes, SVM and C4.5. The obtained results showed the good performance of the LC method, comparing to the problem transformation methods like Binary Relevance and Label Powerset.
Źródło:
Information Systems in Management; 2016, 5, 1; 24-35
2084-5537
2544-1728
Pojawia się w:
Information Systems in Management
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vessel classification method based on vessel behavior in the port of Rotterdam
Autorzy:
Zhou, Y.
Daamen, W.
Vellinga, T.
Hoogendoorn, S.
Powiązania:
https://bibliotekanauki.pl/articles/135299.pdf
Data publikacji:
2015
Wydawca:
Akademia Morska w Szczecinie. Wydawnictwo AMSz
Tematy:
AIS
data analysis
vessel classification
vessel behavior
port
classification method
Opis:
AIS (Automatic Identification System) data have proven to be a valuable source to investigate vessel behavior. The analysis of AIS data provides a possibility to recognize vessel behavior patterns in a waterway area. Furthermore, AIS data can be used to classify vessel behavior into several categories. The analysis results would help the port authority and other equivalent parties in port design and optimization or marine traffic management. For researchers, it provides a systematic way to understand, simulate and predict vessel behavior. This paper focuses on vessel classification in the Botlek area, Rotterdam from the perspective of vessel behavior. In this paper, the vessel properties, including vessel type, GT (Gross Tonnage), length and beam, have been analyzed to investigate the vessel behavior, which is described by four factors including heading, COG (Course over Ground), SOG (Speed over Ground), and position. In order to discover the behavior patterns in normal situations, several thresholds are set in order to filter the collected AIS data to define such situations. By plotting the AIS data, behavioral changes with the changes of properties have been observed. Hence, the correlations between vessel behavior and different vessel properties are investigated. The results reveal that a vessel’s sailing position and COG are both strongly determined by beam, while SOG is affected by GT. For the heading of a vessel, no obvious correlation with any vessel property is found. Each behavioral factor is clustered according to the correlated vessel property. This way, the criteria to classify the vessels are determined. The vessel classification results based on their behavior would likely to lead to more consistency in the analysis, simulation and prediction of the vessel behavior. The reason is that the development of such a simulation model is based on a systematic recognition of the vessel behavior patterns.
Źródło:
Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2015, 42 (114); 86-92
1733-8670
2392-0378
Pojawia się w:
Zeszyty Naukowe Akademii Morskiej w Szczecinie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Charting topographic maps based on UAV data using the image classification method
Autorzy:
Klapa, Przemysław
Bożek, Piotr
Piech, Izabela
Powiązania:
https://bibliotekanauki.pl/articles/100432.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Rolniczy im. Hugona Kołłątaja w Krakowie
Tematy:
topographic map geospatial data
UAV
photogrammetry
large-scale cartographic studies
dane geoprzestrzenne
mapa topograficzna
fotogrametria
wielkoskalowe badanie kartograficzne
Opis:
A topographic map is a representation of the terrain, its landform and spatial elements present therein. Land surveying and photogrammetric measurements must be conducted in order to produce such cartographic document. The following must be done while obtaining information on topographic objects: determine the character and type of an object or phenomenon; determine the range of its occurrence; indicate a precise location. The next stage involves classification of objects into relevant classes and categories, i.e. arable land, pastures, forests, water basins, technical infrastructure, buildings, and other. Then, the determined classes undergo the process of cartographic generalization by combining smaller elements into a single complex, determination of a common border of their occurrence, and application of relevant graphic symbols and colours. The measuring technique which provides quick and accurate topographic information about the surrounding area is the one that uses Unmanned Aerial Vehicles (UAV). Digital photographs taken during the flight are the basis for generating a high-quality orthophotomap. Accurate determination of the location of individual spatial elements allows large-scale cartographic documents to be developed. This paper will present the method of charting topographic maps of rural areas based on orthophotomaps made from the photographs taken during the UAV flight. Supervised and unsupervised methods of object classification will be tested in order to increase the effectiveness of determination of types and occurrence range of individual topographic objects, and the obtained results will be used to chart a topographic map of the studied area.
Źródło:
Geomatics, Landmanagement and Landscape; 2019, 2; 77-85
2300-1496
Pojawia się w:
Geomatics, Landmanagement and Landscape
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification method for vehicles with a maximum permissible weight up to 3.5 tonnes
Autorzy:
Ryguła, A.
Konior, T.
Piwowarczyk, P.
Powiązania:
https://bibliotekanauki.pl/articles/393555.pdf
Data publikacji:
2019
Wydawca:
Polskie Stowarzyszenie Telematyki Transportu
Tematy:
vehicle classification
weigh in motion
data mining
klasyfikacja pojazdów
ważenie w ruchu
eksploracja danych
Opis:
Detecting and distinguishing vehicles with a maximum permissible weight up to 3.5 tonnes, among others required in the TLS 8+1 classification, due to the similar dimensions of selected vehicle groups is often a relatively complex process that requires the use of extensive classification methods. Detection of commercials vans is particularly important. Their parameters are similar to lorry vehicles and their incorrect classification, eg in systems of weighing vehicles in motion, results in the lack of information on exceeding the permissible total weight. The article presents the selected classification method and its effectiveness.
Źródło:
Archives of Transport System Telematics; 2019, 12, 4; 27-30
1899-8208
Pojawia się w:
Archives of Transport System Telematics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Monitoring Vegetation Cover Changes by Sentinel-1 Radar Images Using Random Forest Classification Method
Autorzy:
Tran, Van Anh
Le, Thi Le
Nguyen, Nhu Hung
Le, Thanh Nghi
Tran, Hong Hanh
Powiązania:
https://bibliotekanauki.pl/articles/2020227.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Przeróbki Kopalin
Tematy:
vegetation cover change,
Sentinel-1
Random Forest
Binh Duong
Vietnam
Wietnam
wegetacja
Opis:
Vietnam is an Asian country with hot and humid tropical climate throughout the year. Forests account for more than 40% of the total land area and have a very rich and diverse vegetation. Monitoring the changes in the vegetation cover is obviously important yet challenging, considering such large varying areas and climatic conditions. A traditional remote sensing technique to monitor the vegetation cover involves the use of optical satellite images. However, in presence of the cloud cover, the analyses done using optical satellite image are not reliable. In such a scenario, radar images are a useful alternative due to the ability of radar pulses in penetrating through the clouds, regardless of day or night. In this study, we have used multi temporal C band satellite images to monitor vegetation cover changes for an area in Dau Tieng and Ben Cat districts of Binh Duong province, Mekong Delta, Vietnam. With a collection of 46 images between March 2015 and February 2017, the changes of five land cover types including vegetation loss and replanting in 2017 were analyzed by selecting two cases, using 9 images in the dry season of 3 years 2015, 2016 and 2017 and using all of 46 images to conduct Random Forest classifier with 100, 200, 300 and 500 trees respectively. The result in which the model with nine images and 300 trees gave the best accuracy with an overall accuracy of 98.4% and a Kappa of 0.97. The results demonstrated that using VH polarization, Sentinel-1 gives quite a good accuracy for vegetation cover change. Therefore, Sentinel-1 can also be used to generate reliable land cover maps suitable for different applications.
Źródło:
Inżynieria Mineralna; 2021, 2; 441--451
1640-4920
Pojawia się w:
Inżynieria Mineralna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring
Autorzy:
Wang, Can
Peng, Jianxin
Zhang, Xiaowen
Powiązania:
https://bibliotekanauki.pl/articles/176601.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
acoustical analysis
feature extraction
support vector machine
snoring sound
Opis:
Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.
Źródło:
Archives of Acoustics; 2020, 45, 1; 141-151
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Implementation of Bilinear Separation algorithm as a classification method for SSVEP-based brain-computer interface
Autorzy:
Jukiewicz, M.
Cysewska-Sobusiak, A.
Powiązania:
https://bibliotekanauki.pl/articles/114357.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
brain-computer interface
SSVEP
bilinear separation
support vector machine (SVM)
Opis:
: The aim of this study was to create a two-class brain-computer interface. As in the case of research on SSVEP stimuli flashing at different frequencies were presented to four subjects. Optimal SSVEP recognition results can be obtained from electrodes: O1, O2 and Oz. In this work SVM classifier with Bilinear Separation algorithm have been compared. The best result in the offline tests using Bilinear Separation was: average accuracy of stimuli recognition 93% and ITR 33.1 bit/min, SVM: 90% and 32.8 bit/min.
Źródło:
Measurement Automation Monitoring; 2015, 61, 2; 51-53
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines
Autorzy:
Cui, J.
Shi, G.
Gong, C.
Powiązania:
https://bibliotekanauki.pl/articles/220922.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
power electronics
fault diagnosis
wavelet transforms
support vector machines
directed acyclic graph
nearest neighbours
Opis:
Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.
Źródło:
Metrology and Measurement Systems; 2017, 24, 4; 701-720
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new low SNR underwater acoustic signal classification method based on intrinsic modal features maintaining dimensionality reduction
Autorzy:
Ju, Yang
Wei, Zhengxian
Li, Huangfu
Feng, Xiao
Powiązania:
https://bibliotekanauki.pl/articles/259300.pdf
Data publikacji:
2020
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
acoustic
low SNR
signal classification
feature maintain
dimension reduction
Opis:
The classification of low signal-to-noise ratio (SNR) underwater acoustic signals in complex acoustic environments and increasingly small target radiation noise is a hot research topic. . This paper proposes a new method for signal processing—low SNR underwater acoustic signal classification method (LSUASC)—based on intrinsic modal features maintaining dimensionality reduction. Using the LSUASC method, the underwater acoustic signal was first transformed with the Hilbert-Huang Transform (HHT) and the intrinsic mode was extracted. the intrinsic mode was then transformed into a corresponding Mel-frequency cepstrum coefficient (MFCC) to form a multidimensional feature vector of the low SNR acoustic signal. Next, a semi-supervised fuzzy rough Laplacian Eigenmap (SSFRLE) method was proposed to perform manifold dimension reduction (local sparse and discrete features of underwater acoustic signals can be maintained in the dimension reduction process) and principal component analysis (PCA) was adopted in the proces of dimension reduction to define the reduced dimension adaptively. Finally, Fuzzy C-Means (FCMs), which are able to classify data with weak features was adopted to cluster the signal features after dimensionality reduction. The experimental results presented here show that the LSUASC method is able to classify low SNR underwater acoustic signals with high accuracy.
Źródło:
Polish Maritime Research; 2020, 2; 187-198
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Differentiation between forms of urban development using the object-oriented classification method with Central Warsaw as the example
Autorzy:
Zaremski, Karol
Szmajda, Dorota
Powiązania:
https://bibliotekanauki.pl/articles/2032468.pdf
Data publikacji:
2006-06-01
Wydawca:
Uniwersytet Warszawski. Wydział Geografii i Studiów Regionalnych
Tematy:
object-oriented classification
land cover
land use
urbanized areas
Ikonos
Warsaw
eCognition
segmentation
classification
Opis:
The aim of the paper is to present automated methods of discrimination of urban development forms using object-oriented classification in high-resolution images taken by the Ikonos satellite. The object-oriented classification makes possible to describe individual classes using not only the spectral reflection values but also the shapes, textures and topology of objects. The classification process as such is based on the theory of fuzzy sets. The research covered an area of 25 km,., situated in central Warsaw. As a result of object-oriented classification, five classes of development typical of large cities were distinguished and described.
Źródło:
Miscellanea Geographica. Regional Studies on Development; 2006, 12; 315-327
0867-6046
2084-6118
Pojawia się w:
Miscellanea Geographica. Regional Studies on Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Influence of Classification Method on Efficiency of Modified Synthetic Estimator
Wpływ stosowanej metody klasyfikacji na efektywność modyfikowanego estymatora syntetycznego
Autorzy:
Jurkiewicz, Tomasz
Najman, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/905704.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
small domain estimation
multivariate methods
neural networks
Opis:
The problem of insufficient number of sample observations representing a given population domain of interest (small area) can be solved by applying such estimators, which will be able to combine sample information from the given domain with information about sample units representing other domains. One small area estimation method, called synthetic estimation technique, assumes that the distribution of the variable of interest is identical in the given domain and in the entire population. This assumption, however, is rarely met, and as a result one obtains large estimation errors. In this paper a two-stage estimation procedure is suggested. The first stage consist in applying various classification methods to identify the degree of similarity between the sample units from the investigated domain and sample units representing other domains. In the second stage, those domains, which turned out to be similar to the domain of interest or sample units similar to units from domain of interest, are used to provide sample information with specially constructed weights. Authors present the results of the suggested procedure in an analysis of the continuing vocational training in construction industry based on a sample survey of enterprises. A bootstrap attempt has been made to assess errors of the suggested estimation procedure.
Problem zbyt małej liczby obserwacji w próbie, reprezentującej określoną domenę populacji, może być rozwiązany m. in. poprzez estymatory wykorzystujące informacje o innych jednostkach w próbie. Jedna z metod estymacji dla małych domen, zwana estymacją syntetyczną, zakłada, że rozkład w badanej małej domenie jest identyczny z rozkładem całej populacji. Założenie to pozostaje zazwyczaj niespełnione, zwłaszcza w przypadku specyficznych domen, co skutkuje dużymi błędami estymacji. Problem niespełnienia założeń estymacji syntetycznej może być rozwiązany poprzez zastosowanie dwuetapowego procesu estymacji. W pierwszym etapie za pomocą metod analizy wielowymiarowej, np. za pomocą metody klasyfikacji k-średnich, badania odległości czy też wykorzystując sieci neuronowe typu SOM, określa się podobieństwa domen lub jednostek należących do małej domeny do jednostek z pozostałej części próby. Drugim krokiem jest wykorzystanie w estymacji, za pomocą odpowiednio skonstruowanych wag, informacji tylko o tych jednostkach lub z tych domen, które są podobne do badanej małej domeny. W artykule autorzy przedstawiają rezultaty zastosowanej metody na przykładzie badania reprezentacyjnego kształcenia ustawicznego w branży budowlanej. Za pomocą metod bootsrtrapowych dokonano oceny wpływu stosowania różnych metod badania podobieństw między jednostkami na własności modyfikowanego estymatora syntetycznego.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2006, 196
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
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