Temat: system selection - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Autonomous viewpoint selection of robot based on aesthetic evaluation of a scene
Autorzy:: Lan, K.
Sekiyama, K.
Powiązania:: https://bibliotekanauki.pl/articles/91610.pdf
Data publikacji:: 2016
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: optimal viewpoint selection system
aesthetic evaluation
viewpoint searching
Opis:: In this paper, we propose an optimal viewpoint selection system for monitoring robots to search for the optimal viewpoint of a scene with the highest aesthetic property. Using the information of the targets, we propose a novel method for predicting human aesthetic sense for a scene. We construct evaluation functions based on certain known composition rules using three factors, namely, target size, visual balance, and composition fitting value. Then a score, which is a reflection of human evaluation, will be obtained using these functions. The optimal viewpoint will be selected from a number of candidates around the target group, by evaluating the aesthetic properties of scenes for each candidate viewpoint. Finally, once the optimal viewpoint is confirmed, path planning and path following controls are implemented for the robots during the moving process.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 4; 255-265
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Self-configuring hybrid evolutionary algorithm for fuzzy imbalanced classification with adaptive instance selection
Autorzy:: Stanovov, V.
Semenkin, E.
Semenkina, O.
Powiązania:: https://bibliotekanauki.pl/articles/91578.pdf
Data publikacji:: 2016
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: fuzzy classification
instance selection
genetic fuzzy system
self-configuration
Opis:: A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 3; 173-188
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Bandwidth selection for kernel generalized regression neural networks in identification of hammerstein systems
Autorzy:: Lv, Jiaqing
Pawlak, Mirosław
Powiązania:: https://bibliotekanauki.pl/articles/2031118.pdf
Data publikacji:: 2021
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: generalized regression neural network
nonparametric estimation
bandwidth
data-driven selection
nonlinear system
Hammerstein system
Opis:: This paper addresses the issue of data-driven smoothing parameter (bandwidth) selection in the context of nonparametric system identification of dynamic systems. In particular, we examine the identification problem of the block-oriented Hammerstein cascade system. A class of kernel-type Generalized Regression Neural Networks (GRNN) is employed as the identification algorithm. The statistical accuracy of the kernel GRNN estimate is critically influenced by the choice of the bandwidth. Given the need of data-driven bandwidth specification we propose several automatic selection methods that are compared by means of simulation studies. Our experiments reveal that the method referred to as the partitioned cross-validation algorithm can be recommended as the practical procedure for the bandwidth choice for the kernel GRNN estimate in terms of its statistical accuracy and implementation aspects.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 3; 181-194
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Feature selection using particle swarm optimization in text categorization
Autorzy:: Aghdam, M. H.
Heidari, S.
Powiązania:: https://bibliotekanauki.pl/articles/91792.pdf
Data publikacji:: 2015
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: classification system
feature selection
text categorization
particle swarm optimization (PSO)
system klasyfikacji
wybór funkcji
kategoryzacja tekstu
optymalizacja rojem cząstek
Opis:: Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 231-238
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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