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Titlebook: Artificial Intelligence and Soft Computing; 13th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

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楼主: Ferret
发表于 2025-3-25 06:50:56 | 显示全部楼层
Face Classification Based on Linguistic Description of Facial Features knowledge representation, fuzzy sets, fuzzy IF-THEN rules, and fuzzy granulation are employed. Features of face elements (nose, eyes, etc.) are extracted by use of existing detection techniques, such as measurements of horizontal and vertical sizes. Linguistic variables that correspond to fuzzy set
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Impact of Bayesian Network Model Structure on the Accuracy of Medical Diagnostic Systemsrmed that overall precision of their probabilities is not crucial. In this paper, we study the impact of the structure of a Bayesian network on the precision of medical diagnostic systems. We show that also the structure is not that important – diagnostic accuracy of several medical diagnostic model
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A New Three-Dimensional Facial Landmarks in Recognitionive to those using flat images; however, they are much more complex. In this paper, we present subsequent results of our research on a new representation of characteristic points for the 3D face. As a comparative method the standard PCA is applied.
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https://doi.org/10.1007/978-3-662-58451-4s for image categorization task. In Bag of Visual Words framework, image is represented as vector of frequencies of Visual Words, typically of length from hundreds to thousands elements. A dictionary of Visual Words is produced from image keypoints detected by SIFT algorithm and quantized into words
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Primärprophylaxe venöser Thromboembolien, the providers seek to improve prediction models in an effort to intervene with potential churners. Many studies have evaluated different models seeking any improvement to prediction accuracy. This study proposes that the attributes, not the model, need to be reconsidered. By representing call deta
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Pädiatrische und geriatrische HNO-Heilkundempression and prediction accuracy. The examined instance selection algorithms for classification are ENN, CNN, RNG and GE and for regression are the developed by us Generalized CNN and Generalized ENN algorithms. Results of the comparative experimental study performed using different configurations
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