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Wyszukujesz frazę "Support Vector Machines" wg kryterium: Temat


Wyświetlanie 1-6 z 6
Tytuł:
Fetal state evaluation with fuzzy analysis of newborn attributes using CUDA architecture
Autorzy:
Czabański, R.
Wróbel, J.
Jeżewski, J.
Powiązania:
https://bibliotekanauki.pl/articles/333255.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
fuzzy systems
fetal monitoring
support vector machines
CUDA architecture
systemy rozmyte
monitorowanie płodu
architektura CUDA
Opis:
Cardiotocography is a biophysical method of fetal state evaluation involving the recording and analysis of the fetal heart rate (FHR). Since a proper interpretation of the signal is relatively difficult, an automatic classification is often based on computational intelligence methods. The quality of classifiers based on supervised learning algorithms depends on a proper selection of learning data. In case of the fetal state evaluation, the learning is usually based on a set of quantitative parameters of FHR signal and the corresponding reference information determined on the basis of the retrospective analysis of newborn attributes. Values of the single attribute have been used so far as a reference. As a result, a part of information on the actual neonatal outcome has always been lost. The following paper presents a method of the fuzzy reasoning leading to an evaluation of neonatal outcome as a function of three newborn attributes. The fuzzy system was used in the process of a qualitative evaluation of the fetal state based on quantitative analysis of FHR signal using a support vector machine (SVM). In order to improve computational effectiveness, the learning algorithm was implemented in Compute Unified Device Architecture (CUDA). The results of these studies confirm the effectiveness of the proposed method and indicate the possibility of practical usage of the fuzzy system in supervised learning algorithms for the qualitative evaluation of the fetal state.
Źródło:
Journal of Medical Informatics & Technologies; 2013, 22; 125-133
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Genetic algorithms for classifiers training sets optimisation applied to human face recognition
Autorzy:
Kawulok, M.
Powiązania:
https://bibliotekanauki.pl/articles/333826.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
maszyna wektorów nośnych
algorytmy genetyczne
rozpoznawanie twarzy człowieka
support vector machines
genetic algorithms
human face recognition
Opis:
Human face recognition is a multi-stage process within which many classification problems must be solved. This is performed by learning machines which elaborate classification rules based on a given training set. Therefore, one of the most important issues is selection of a training set which would properly represent the data that will be further classified. This paper presents an approach which utilizes genetic algorithms for selecting classifiers' training sets. This approach was implemented for the Support Vector Machines which is applied in two areas of automatic human face recognition: face verification and feature vectors comparison. Effectiveness of the presented concept was confirmed with appropriate experiments which results are described in this paper.
Źródło:
Journal of Medical Informatics & Technologies; 2007, 11; 135-143
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving the efficacy of automated fetal state assessment with fuzzy analysis of delivery outcome
Autorzy:
Czabanski, R.
Jezewski, M.
Horoba, K.
Jezewski, J.
Leski, J.
Powiązania:
https://bibliotekanauki.pl/articles/333655.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
fetal monitoring
fuzzy inference
support vector machines
supervised learning
monitorowanie płodu
wnioskowanie rozmyte
maszyna wektorów nośnych
uczenie nadzorowane
Opis:
A number of methods of the qualitative assessment of fetal heart rate (FHR) signals are based on supervised learning. The classification methods based on the supervised learning require a set of training recordings accompanied by the reference interpretation. In the real data collections the class of signals related to fetal distress is usually under-represented. Too small percentage of distress patterns adversely affects the effectiveness of the automated evaluation of the fetal state. The paper presents a method of equalizing the class sizes based on the reference assessment of the fetal state with the fuzzy analysis of the newborn attributes. The supervised learning with increased number of the FHR signals, which are characterized by the highest rate of the fuzzy inference leads to significant increase of the efficacy of the qualitative assessment of the fetal state using the Lagrangian support vector machine.
Źródło:
Journal of Medical Informatics & Technologies; 2015, 24; 223-230
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
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:
Using Multiclass SVM methods for classification of DNA microarray data
Autorzy:
Student, S.
Powiązania:
https://bibliotekanauki.pl/articles/333907.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
metoda cząstkowych najmniejszych kwadratów
maszyna wektorów nośnych
Partial Least Squares PLS
dimension reductions
MMulticlass Support Vector Machines MSVM
One-Versus-One OvO
One-Versus RestOvR
Opis:
One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. A classifier using Multiclass SVM [4] (Support Vector Machines) is described in this article. Our classifier involves dimension reduction using Multivariate Partial Least Squares (MPLS) for classification more than two classes. We use also two methods based on binary classifications: One-Against-All [5] and One-Against-One [6]. These three methods have been tested on a data set involving 125 tumor/normal thyroid human DNA microarrays samples. There are 66 Papillary throid carcinoma, 32 follicular throid carcinoma and 27 normal tissues. The most important thing is to find small number of genes that discriminate between these three classes with good accuracy. The best genes can be selected for Q-PCR validation. Molecular markers differentiating between throid cancer and normal tissues can help in clinical diagnostics and therapy methods. For error estimation we are use the bootstrap .632 [8] technique. Major issue with bootstrap estimators is their high computational cost. That is why we use a OpenMosix with MPI (Message Passing Interface) cluster technology for this system for parallel computation space.
Źródło:
Journal of Medical Informatics & Technologies; 2007, 11; 197-204
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Support Vector Machines in automatic human face recognition
Autorzy:
Kawulok, M.
Powiązania:
https://bibliotekanauki.pl/articles/333790.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
automatyczne rozpoznanie twarzy
metoda wektorów nośnych
wykrywanie twarzy
wybór cech
fuzja wielometodowa
automatic face recognition
support vector machines
face detection
feature extraction
multi-method fusion
Opis:
This paper presents the possibilities of applying the Support Vector Machines (SVM) in the process of automatic human face recognition. It is described how the existing methods of face recognition can be improved by the SVM. Moreover, a new approach to the multi-method fusion utilising the SVM is proposed. Usefulness of all the methods described in the paper improving the face recognition effectiveness by the SVM is confirmed by the experimental results.
Źródło:
Journal of Medical Informatics & Technologies; 2005, 9; 143-150
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-6 z 6

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