Wszystkie pola: fuzzy methods - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: An ε-Insensitive Approach to Fuzzy Clustering
Autorzy:: Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/908067.pdf
Data publikacji:: 2001
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: programowanie
metoda grupowania
fuzzy clustering
fuzzy c-means
robust methods
varepsilon-insensitivity
fuzzy c-medians
Opis:: Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new varepsilon-insensitive Fuzzy C-Means (varepsilonFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2001, 11, 4; 993-1007
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Improving the Generalization Ability of Neuro-Fuzzy Systems by e-Insensitive Learning
Autorzy:: Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/908037.pdf
Data publikacji:: 2002
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: informatyka
fuzzy systems
neural networks
tolerant learning
generalization control
robust methods
Opis:: A new learning method tolerant of imprecision is introduced and used in neuro-fuzzy modelling. The proposed method makes it possible to dispose of an intrinsic inconsistency of neuro-fuzzy modelling, where zero-tolerance learning is used to obtain a fuzzy model tolerant of imprecision. This new method can be called e-insensitive learning, where, in order to fit the fuzzy model to real data, the e-insensitive loss function is used. e-insensitive learning leads to a model with minimal Vapnik-Chervonenkis dimension, which results in an improved generalization ability of this system. Another advantage of the proposed method is its robustness against outliers. This paper introduces two approaches to solving e-insensitive learning problem. The first approach leads to a quadratic programming problem with bound constraints and one linear equality constraint. The second approach leads to a problem of solving a system of linear inequalities. Two computationally efficient numerical methods for e-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2002, 12, 3; 437-447
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Improving the quality of the fetal state assessment with epsilon-insensitive learning methods
Autorzy:: Czabański, R.
Wróbel, J.
Jeżewski, J.
Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333468.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: fetal monitoring
fuzzy implication
epsilon-insensitive learning
monitorowanie płodu
implikacja rozmyta
Opis:: Recording and analysis of fetal heart rate (FHR) signal is nowadays the primary method for the biophysical assessment of the fetal state. Since the correct interpretation of crucial FHR characteristics is difficult, methods of automated quantitative signal evaluation are still the subject of the research studies. In the following paper we investigated the possibility of improvement of the fetal state evaluation on the basis of the epsilon-insensitive learning (eIL). We examined two eIL procedures integrated with fuzzy clustering algorithms as well as different methods of logical interpretation of the fuzzy conditional statements. The quality of the FHR signal classification was evaluated using the data collected with the computerized fetal surveillance system. The learning performance was measured with the number of correct classification (CC) and overall quality index (QI) defined as a geometric mean of sensitivity and specificity. The obtained results (CC = 88 % and QI = 87 %) show a high efficiency of the fetal state assessment using the epsilon-insensitive learning based methods.
Źródło:: Journal of Medical Informatics & Technologies; 2014, 23; 19-26
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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